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Fulltext Predicting Return to Work after Cardiac Rehabilitation using Machine Learning Models

Yuan CJ, Varathan KD, Suhaimi A, Ling LW

J Rehabil Med, 2023 Jan 09;55:jrm00348.
PMID: 36306152 DOI: 10.2340/jrm.v54.2432

OBJECTIVE: To explore machine learning models for predicting return to work after cardiac rehabilitation.
SUBJECTS: Patients who were admitted to the University of Malaya Medical Centre due to cardiac events.
METHODS: Eight different machine learning models were evaluated. The models included 3 different sets of features: full features; significant features from multiple logistic regression; and features selected from recursive feature extraction technique. The performance of the prediction models with each set of features was compared.
RESULTS: The AdaBoost model with the top 20 features obtained the highest performance score of 92.4% (area under the curve; AUC) compared with other prediction models.
CONCLUSION: The findings showed the potential of using machine learning models to predict return to work after cardiac rehabilitation.

Matched MeSH terms: Machine Learning
Fulltext Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms

Ling L, Aldoghachi AF, Chong ZX, Ho WY, Yeap SK, Chin RJ, et al.

Int J Mol Sci, 2022 Dec 06;23(23).
PMID: 36499713 DOI: 10.3390/ijms232315382

Detecting breast cancer (BC) at the initial stages of progression has always been regarded as a lifesaving intervention. With modern technology, extensive studies have unraveled the complexity of BC, but the current standard practice of early breast cancer screening and clinical management of cancer progression is still heavily dependent on tissue biopsies, which are invasive and limited in capturing definitive cancer signatures for more comprehensive applications to improve outcomes in BC care and treatments. In recent years, reviews and studies have shown that liquid biopsies in the form of blood, containing free circulating and exosomal microRNAs (miRNAs), have become increasingly evident as a potential minimally invasive alternative to tissue biopsy or as a complement to biomarkers in assessing and classifying BC. As such, in this review, the potential of miRNAs as the key BC signatures in liquid biopsy are addressed, including the role of artificial intelligence (AI) and machine learning platforms (ML), in capitalizing on the big data of miRNA for a more comprehensive assessment of the cancer, leading to practical clinical utility in BC management.

Matched MeSH terms: Machine Learning
Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Singh NK, Yadav M, Singh V, Padhiyar H, Kumar V, Bhatia SK, et al.

Bioresour Technol, 2023 Feb;369:128486.
PMID: 36528177 DOI: 10.1016/j.biortech.2022.128486

Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.

Matched MeSH terms: Machine Learning
Fulltext Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images

Voon W, Hum YC, Tee YK, Yap WS, Salim MIM, Tan TS, et al.

Sci Rep, 2022 Nov 10;12(1):19200.
PMID: 36357456 DOI: 10.1038/s41598-022-21848-3

Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that deep learning may achieve reliable accuracy in IDC grade classification using histopathology images. However, there is a dearth of comprehensive performance comparisons of Convolutional Neural Network (CNN) designs on IDC in the literature. As such, we would like to conduct a comparison analysis of the performance of seven selected CNN models: EfficientNetB0, EfficientNetV2B0, EfficientNetV2B0-21k, ResNetV1-50, ResNetV2-50, MobileNetV1, and MobileNetV2 with transfer learning. To implement each pre-trained CNN architecture, we deployed the corresponded feature vector available from the TensorFlowHub, integrating it with dropout and dense layers to form a complete CNN model. Our findings indicated that the EfficientNetV2B0-21k (0.72B Floating-Point Operations and 7.1 M parameters) outperformed other CNN models in the IDC grading task. Nevertheless, we discovered that practically all selected CNN models perform well in the IDC grading task, with an average balanced accuracy of 0.936 ± 0.0189 on the cross-validation set and 0.9308 ± 0.0211on the test set.

Matched MeSH terms: Machine Learning
Nitrate Classification Based on Optical Absorbance Data Using Machine Learning Algorithms for a Hydroponics System

Sulaiman R, Azeman NH, Abu Bakar MH, Ahmad Nazri NA, Masran AS, Ashrif A Bakar A

Appl Spectrosc, 2023 Feb;77(2):210-219.
PMID: 36348500 DOI: 10.1177/00037028221140924

Nutrient solution plays an essential role in providing macronutrients to hydroponic plants. Determining nitrogen in the form of nitrate is crucial, as either a deficient or excessive supply of nitrate ions may reduce the plant yield or lead to environmental pollution. This work aims to evaluate the performance of feature reduction techniques and conventional machine learning (ML) algorithms in determining nitrate concentration levels. Two features reduction techniques, linear discriminant analysis (LDA) and principal component analysis (PCA), and seven ML algorithms, for example, k-nearest neighbors (KNN), support vector machine, decision trees, naïve bayes, random forest (RF), gradient boosting, and extreme gradient boosting, were evaluated using a high-dimensional spectroscopic dataset containing measured nitrate-nitrite mixed solution absorbance data. Despite the limited and uneven number of samples per class, this study demonstrated that PCA outperformed LDA on the high-dimensional spectroscopic dataset. The classification accuracy of ML algorithms combined with PCA ranged from 92.7% to 99.8%, whereas the classification accuracy of ML algorithms combined with LDA ranged from 80.7% to 87.6%. The PCA with the RF algorithm exhibited the best performance with 99.8% accuracy.

Matched MeSH terms: Machine Learning
Fulltext Social media use in higher education: Building a structural equation model for student satisfaction and performance

Al-Rahmi AM, Shamsuddin A, Wahab E, Al-Rahmi WM, Alyoussef IY, Crawford J

Front Public Health, 2022;10:1003007.
PMID: 36203665 DOI: 10.3389/fpubh.2022.1003007

Social media utilization at the student-level has become more prevalent contemporary higher education. Hence, this study is aimed at developing a specific model, along with the behavioral intention to use, to explore educational quality, actual social media use, and task-technology fit that affects student satisfaction and performance impact through examining the synergies of constructivism, user acceptance and usage of information technology, and technology acceptance. To test, a survey was administered to 430 students across five Malaysian universities. Through structural equation modeling, findings indicate that to improve student satisfaction and student performance through embedded social media, students need to have opportunities to collaborate on learning, have easy access to social media, perceive such use to be easy, and have aligned expectation on performance and effort. Interestingly, the actual social media use, was the only variable in the model that did not predict student satisfaction, despite its role in predicting student performance. The study highlights that constructivist learning, as well as task-technology fit over social media, enhances the students' learning experience and enables knowledge sharing and dissemination. The effect of using social media on student satisfaction and academic performance highlights that all students think that it is adequate for their instructors to improve their usage of social media tools. Therefore, we advocate learners and students employing social media for academic purposes with the help of lecturers at higher teaching organizations and institutions.

Matched MeSH terms: Learning
Fulltext An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals

Khare SK, Acharya UR

Comput Biol Med, 2023 Mar;155:106676.
PMID: 36827785 DOI: 10.1016/j.compbiomed.2023.106676

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects a person's sleep, mood, anxiety, and learning. Early diagnosis and timely medication can help individuals with ADHD perform daily tasks without difficulty. Electroencephalogram (EEG) signals can help neurologists to detect ADHD by examining the changes occurring in it. The EEG signals are complex, non-linear, and non-stationary. It is difficult to find the subtle differences between ADHD and healthy control EEG signals visually. Also, making decisions from existing machine learning (ML) models do not guarantee similar performance (unreliable).
METHOD: The paper explores a combination of variational mode decomposition (VMD), and Hilbert transform (HT) called VMD-HT to extract hidden information from EEG signals. Forty-one statistical parameters extracted from the absolute value of analytical mode functions (AMF) have been classified using the explainable boosted machine (EBM) model. The interpretability of the model is tested using statistical analysis and performance measurement. The importance of the features, channels and brain regions has been identified using the glass-box and black-box approach. The model's local and global explainability has been visualized using Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Morris sensitivity. To the best of our knowledge, this is the first work that explores the explainability of the model prediction in ADHD detection, particularly for children.
RESULTS: Our results show that the explainable model has provided an accuracy of 99.81%, a sensitivity of 99.78%, 99.84% specificity, an F-1 measure of 99.83%, the precision of 99.87%, a false detection rate of 0.13%, and Mathew's correlation coefficient, negative predicted value, and critical success index of 99.61%, 99.73%, and 99.66%, respectively in detecting the ADHD automatically with ten-fold cross-validation. The model has provided an area under the curve of 100% while the detection rate of 99.87% and 99.73% has been obtained for ADHD and HC, respectively.
CONCLUSIONS: The model show that the interpretability and explainability of frontal region is highest compared to pre-frontal, central, parietal, occipital, and temporal regions. Our findings has provided important insight into the developed model which is highly reliable, robust, interpretable, and explainable for the clinicians to detect ADHD in children. Early and rapid ADHD diagnosis using robust explainable technologies may reduce the cost of treatment and lessen the number of patients undergoing lengthy diagnosis procedures.

Matched MeSH terms: Machine Learning
Fulltext Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Shaikh AK, Nazir A, Khan I, Shah AS

Sci Rep, 2022 Dec 29;12(1):22562.
PMID: 36581655 DOI: 10.1038/s41598-022-26499-y

Smart grids and smart homes are getting people's attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with "day as covariates" remained better than the 1, 2, 3, and 4-week scenarios.

Matched MeSH terms: Machine Learning
Fulltext Efficacy and pitfalls of digital technologies in healthcare services: A systematic review of two decades

Akhtar N, Khan N, Qayyum S, Qureshi MI, Hishan SS

Front Public Health, 2022;10:869793.
PMID: 36187628 DOI: 10.3389/fpubh.2022.869793

The use of technology in the healthcare sector and its medical practices, from patient record maintenance to diagnostics, has significantly improved the health care emergency management system. At that backdrop, it is crucial to explore the role and challenges of these technologies in the healthcare sector. Therefore, this study provides a systematic review of the literature on technological developments in the healthcare sector and deduces its pros and cons. We curate the published studies from the Web of Science and Scopus databases by using PRISMA 2015 guidelines. After mining the data, we selected only 55 studies for the systematic literature review and bibliometric analysis. The study explores four significant classifications of technological development in healthcare: (a) digital technologies, (b) artificial intelligence, (c) blockchain, and (d) the Internet of Things. The novel contribution of current study indicate that digital technologies have significantly influenced the healthcare services such as the beginning of electronic health record, a new era of digital healthcare, while robotic surgeries and machine learning algorithms may replace practitioners as future technologies. However, a considerable number of studies have criticized these technologies in the health sector based on trust, security, privacy, and accuracy. The study suggests that future studies, on technological development in healthcare services, may take into account these issues for sustainable development of the healthcare sector.

Matched MeSH terms: Machine Learning
Air pollution and cardiorespiratory hospitalization, predictive modeling, and analysis using artificial intelligence techniques

Usmani RSA, Pillai TR, Hashem IAT, Marjani M, Shaharudin R, Latif MT

Environ Sci Pollut Res Int, 2021 Oct;28(40):56759-56771.
PMID: 34075501 DOI: 10.1007/s11356-021-14305-7

Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. One of the major effects of air pollution on health is hospitalizations associated with air pollution. Recently, the estimation and prediction of air pollution-based hospitalization is carried out using artificial intelligence (AI) and machine learning (ML) techniques, i.e., deep learning and long short-term memory (LSTM). However, there is ample room for improvement in the available applied methodologies to estimate and predict air pollution-based hospital admissions. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the artificial intelligence (AI) techniques. We propose the enhanced long short-term memory (ELSTM) model and provide a comparison with other AI techniques, i.e., LSTM, DL, and vector autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The utilized dataset contains the data from January 2006 to December 2016 for five study locations, i.e., Klang (KLN), Shah Alam (SA), Putrajaya (PUJ), Petaling Jaya (PJ), and Cheras, Kuala Lumpur (CKL). The dataset for Banting contains data from April 2010 to December 2016, and the data for Batu Muda, Kuala Lumpur, contains data from January 2009 to December 2016. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.

Matched MeSH terms: Machine Learning
A deep neural network-based approach for prediction of mutagenicity of compounds

Kumar R, Khan FU, Sharma A, Siddiqui MH, Aziz IB, Kamal MA, et al.

Environ Sci Pollut Res Int, 2021 Sep;28(34):47641-47650.
PMID: 33895950 DOI: 10.1007/s11356-021-14028-9

We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound's safety. Exposure and handling of mutagenic chemicals in the environment pose a high health risk; therefore, identification and screening of these chemicals are essential. Considering the time constraints and the pressure to avoid laboratory animals' use, the shift to alternative methodologies that can establish a rapid and cost-effective detection without undue over-conservation seems critical. In this regard, computational detection and identification of the mutagens in environmental samples like drugs, pesticides, dyes, reagents, wastewater, cosmetics, and other substances is vital. From the last two decades, there have been numerous efforts to develop the prediction models for mutagenicity, and by far, machine learning methods have demonstrated some noteworthy performance and reliability. However, the accuracy of such prediction models has always been one of the major concerns for the researchers working in this area. The mutagenicity prediction models were developed using deep neural network (DNN), support vector machine, k-nearest neighbor, and random forest. The developed classifiers were based on 3039 compounds and validated on 1014 compounds; each of them encoded with 1597 molecular feature vectors. DNN-based prediction model yielded highest prediction accuracy of 92.95% and 83.81% with the training and test data, respectively. The area under the receiver's operating curve and precision-recall curve values were found to be 0.894 and 0.838, respectively. The DNN-based classifier not only fits the data with better performance as compared to traditional machine learning algorithms, viz., support vector machine, k-nearest neighbor, and random forest (with and without feature reduction) but also yields better performance metrics. In current work, we propose a DNN-based model to predict mutagenicity of compounds.

Matched MeSH terms: Machine Learning
Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index

Abba SI, Pham QB, Saini G, Linh NTT, Ahmed AN, Mohajane M, et al.

Environ Sci Pollut Res Int, 2020 Nov;27(33):41524-41539.
PMID: 32686045 DOI: 10.1007/s11356-020-09689-x

In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI) at three stations, namely Nizamuddin, Palla, and Udi (Chambal), across the Yamuna River, India. The nonlinear ensemble technique was proposed using the neural network ensemble (NNE) approach to improve the performance accuracy of the single models. The observed WQ parameters were provided by the Central Pollution Control Board (CPCB) including dissolved oxygen (DO), pH, biological oxygen demand (BOD), ammonia (NH3), temperature (T), and WQI. The performance of the models was evaluated by various statistical indices. The obtained results indicated the feasibility of the developed data intelligence models for predicting the WQI at the three stations with the superior modelling results of the NNE. The results also showed that the minimum values for root mean square (RMS) varied between 0.1213 and 0.4107, 0.003 and 0.0367, and 0.002 and 0.0272 for Nizamuddin, Palla, and Udi (Chambal), respectively. ANFIS-M3, BPNN-M4, and BPNN-M3 improved the performance with regard to an absolute error by 41%, 4%, and 3%, over other models for Nizamuddin, Palla, and Udi (Chambal) stations, respectively. The predictive comparison demonstrated that NNE proved to be effective and can therefore serve as a reliable prediction approach. The inferences of this paper would be of interest to policymakers in terms of WQ for establishing sustainable management strategies of water resources.

Matched MeSH terms: Machine Learning
Fulltext Opportunities and challenges of 5G network technology toward precision medicine

Kang CC, Lee TY, Lim WF, Yeo WWY

Clin Transl Sci, 2023 Nov;16(11):2078-2094.
PMID: 37702288 DOI: 10.1111/cts.13640

Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G-enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient-centric care via a precision medicine approach.

Matched MeSH terms: Machine Learning
Fulltext Simulation via instant messaging - Birmingham advance (SIMBA): an innovative simulation-based learning model that helped to keep medical education continue during the COVID-19 pandemic

Morgan G, Ooi E, Hanania T, Davitadze M, Zhou D, Blaggan P, et al.

Clin Med (Lond), 2021 Mar;21(Suppl 2):34-35.
PMID: 34078689 DOI: 10.7861/clinmed.21-2-s34
Matched MeSH terms: Learning
Fulltext Evaluation of a mentored student project programme using reflective summaries

Devi V, Abraham RR

Natl Med J India, 2021 3 24;33(2):102-106.
PMID: 33753639 DOI: 10.4103/0970-258X.310920

Background: . Undergraduate research experience has become increasingly relevant for today's medical students, considering the professional requirements of their challenging future.
Methods: . In the mentored student project (MSP) programme at Melaka Manipal Medical College, students undertake a short-term group research project under the guidance of their mentor. After data collection and analysis, students are required to write an abstract, present a poster and also write individual reflective summaries of their research experience. We evaluated the MSP programme using reflective summaries of a batch of undergraduate medical students. Data from 41 reflective summaries were analysed using the thematic analysis approach. The learning outcomes at the third and fourth levels of the Kirkpatrick evaluation model were determined from the summaries.
Results: . Students' reflective summaries indicated that they were satisfied with the MSP experience. In all the summaries, there was a mention of an improvement in teamwork skills through MSP. Improved relations with mentors were another relevant outcome. Improvement in communication skills and a positive change related to research attitude were also reported by students.
Conclusions: . Reflective summaries as a means to evaluate the MSP programme was found to be an easy, feasible and cost-effective method. The qualitative approach adopted for data analysis enabled the programme coordinators to assess the strengths and barriers of the programme.

Matched MeSH terms: Learning
Convolutional neural network-multi-kernel radial basis function neural network-salp swarm algorithm: a new machine learning model for predicting effluent quality parameters

Sheikh Khozani Z, Ehteram M, Mohtar WHMW, Achite M, Chau KW

Environ Sci Pollut Res Int, 2023 Sep;30(44):99362-99379.
PMID: 37610542 DOI: 10.1007/s11356-023-29406-8

A wastewater treatment plant (WWTP) is an essential part of the urban water cycle, which reduces concentration of pollutants in the river. For monitoring and control of WWTPs, researchers develop different models and systems. This study introduces a new deep learning model for predicting effluent quality parameters (EQPs) of a WWTP. A method that couples a convolutional neural network (CNN) with a novel version of radial basis function neural network (RBFNN) is proposed to simultaneously predict and estimate uncertainty of data. The multi-kernel RBFNN (MKRBFNN) uses two activation functions to improve the efficiency of the RBFNN model. The salp swarm algorithm is utilized to set the MKRBFNN and CNN parameters. The main advantage of the CNN-MKRBFNN-salp swarm algorithm (SSA) is to automatically extract features from data points. In this study, influent parameters (if) are used as inputs. Biological oxygen demand (BODif), chemical oxygen demand (CODif), total suspended solids (TSSif), volatile suspended solids (VSSif), and sediment (SEDef) are used to predict EQPs, including CODef, BODef, and TSSef. At the testing level, the Nash-Sutcliffe efficiencies of CNN-MKRBFNN-SSA are 0.98, 0.97, and 0.98 for predicting CODef, BODef, and TSSef. Results indicate that the CNN-MKRBFNN-SSA is a robust model for simulating complex phenomena.

Matched MeSH terms: Machine Learning
Impact of air pollutants on climate change and prediction of air quality index using machine learning models

Ravindiran G, Rajamanickam S, Kanagarathinam K, Hayder G, Janardhan G, Arunkumar P, et al.

Environ Res, 2023 Dec 15;239(Pt 1):117354.
PMID: 37821071 DOI: 10.1016/j.envres.2023.117354

The impact of air pollution in Chennai metropolitan city, a southern Indian coastal city was examined to predict the Air Quality Index (AQI). Regular monitoring and prediction of the Air Quality Index (AQI) are critical for combating air pollution. The current study created machine learning models such as XGBoost, Random Forest, BaggingRegressor, and LGBMRegressor for the prediction of the AQI using the historical data available from 2017 to 2022. According to historical data, the AQI is highest in January, with a mean value of 104.6 g/gm, and the lowest in August, with a mean AQI value of 63.87 g/gm. Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. The log transformation method is used to normalize datasets and determine skewness and kurtosis. The XGBoost model demonstrated strong performance, achieving an R2 (correlation coefficient) of 0.9935, a mean absolute error (MAE) of 0.02, a mean square error (MSE) of 0.001, and a root mean square error (RMSE) of 0.04. In comparison, the LightGBM model's prediction was less effective, as it attained an R2 of 0.9748. According to the study, the AQI in Chennai has been increasing over the last two years, and if the same conditions persist, the city's air pollution will worsen in the future. Furthermore, accurate future air quality level predictions can be made using historical data and advanced machine learning algorithms.

Matched MeSH terms: Machine Learning
Machine learning approaches in diagnosing tuberculosis through biomarkers - A systematic review

Balakrishnan V, Kherabi Y, Ramanathan G, Paul SA, Tiong CK

Prog Biophys Mol Biol, 2023 May;179:16-25.
PMID: 36931609 DOI: 10.1016/j.pbiomolbio.2023.03.001

Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two algorithms, with the highest accuracy, sensitivity and specificity reported to be 97.0%, 99.2%, and 98.0%, respectively. Further, protein-based biomarkers were widely explored, followed by gene-based such as RNA sequence and, Spoligotypes. Publicly available datasets were observed to be popularly used by the studies reviewed whilst studies targeting specific cohorts such as HIV patients or children gathering their own data from healthcare facilities, leading to smaller datasets. Of these, most studies used the leave one out cross validation technique to mitigate overfitting. The review shows that machine learning is increasingly assessed in research to improve TB diagnosis through biomarkers, as promising results were shown in terms of model's detection performance. This provides insights on the possible application of machine learning approaches to diagnose TB using biomarkers as opposed to the traditional methods that can be time consuming. Low-middle income settings, where access to basic biomarkers could be provided as compared to sputum-based tests that are not always available, could be a major application of such models.

Matched MeSH terms: Machine Learning
Fulltext Earthquake-Induced Building-Damage Mapping Using Explainable AI (XAI)

Matin SS, Pradhan B

Sensors (Basel), 2021 Jun 30;21(13).
PMID: 34209169 DOI: 10.3390/s21134489

Building-damage mapping using remote sensing images plays a critical role in providing quick and accurate information for the first responders after major earthquakes. In recent years, there has been an increasing interest in generating post-earthquake building-damage maps automatically using different artificial intelligence (AI)-based frameworks. These frameworks in this domain are promising, yet not reliable for several reasons, including but not limited to the site-specific design of the methods, the lack of transparency in the AI-model, the lack of quality in the labelled image, and the use of irrelevant descriptor features in building the AI-model. Using explainable AI (XAI) can lead us to gain insight into identifying these limitations and therefore, to modify the training dataset and the model accordingly. This paper proposes the use of SHAP (Shapley additive explanation) to interpret the outputs of a multilayer perceptron (MLP)-a machine learning model-and analyse the impact of each feature descriptor included in the model for building-damage assessment to examine the reliability of the model. In this study, a post-event satellite image from the 2018 Palu earthquake was used. The results show that MLP can classify the collapsed and non-collapsed buildings with an overall accuracy of 84% after removing the redundant features. Further, spectral features are found to be more important than texture features in distinguishing the collapsed and non-collapsed buildings. Finally, we argue that constructing an explainable model would help to understand the model's decision to classify the buildings as collapsed and non-collapsed and open avenues to build a transferable AI model.

Matched MeSH terms: Machine Learning
A phenomenological study on East and Southeast Asian dental educators: perceived importance, challenges, and strategies in teaching dental materials science

Lin GSS, Tan WW, Foong CC

BMC Oral Health, 2023 Aug 13;23(1):571.
PMID: 37574553 DOI: 10.1186/s12903-023-03293-4

BACKGROUND: Effective teaching of dental materials science is crucial for dental students to develop a comprehensive understanding of materials used in clinical practice. However, literature on educators' views on teaching this subject is still scarce. This qualitative study aimed to explore the lived experiences of dental educators in teaching dental materials science subjects, thereby addressing potential gaps and enhancing teaching practices.
METHODS: Thirteen dental educators from East and Southeast Asian countries (Malaysia, China, Indonesia, Thailand, South Korea, and Japan) participated in the present study. The present study adopted a transcendental phenomenological approach. One-to-one semi-structured online interviews were conducted. Interviews were recorded and transcribed verbatim. Thematic analysis was employed to identify patterns in the educators' experiences.
RESULTS: Three themes emerged from the present study. First, perceptions of the importance of dental materials science, highlighting its relevance in clinical practice, patient care, and lifelong learning. Second, the challenges faced in teaching dental materials science include limited instructional time, complex content, and insufficient resources. Third, specific strategies, such as applying interactive teaching methods, integrating clinical scenarios, and promoting critical thinking skills have been suggested to enhance teaching and learning.
CONCLUSION: Understanding dental educators' experiences can improve dental materials science education, curriculum development, teaching methods, and faculty training programmes, ultimately enhancing the knowledge and skills of dental students in this field.

Matched MeSH terms: Learning

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