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  1. Nonclinical Features in Predictive Modeling of Cardiovascular Diseases: A Machine Learning Approach
    Sajid MR, Muhammad N, Zakaria R, Shahbaz A, Bukhari SAC, Kadry S, et al.
    Interdiscip Sci, 2021 Jun;13(2):201-211.
    PMID: 33675528 DOI: 10.1007/s12539-021-00423-w
    BACKGROUND: In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This situation allows researchers to improve CVDs prediction models using new features and risk computing methods. This study aims to assess nonclinical features that can be easily available in any healthcare systems, in predicting CVDs using advanced and flexible machine learning (ML) algorithms.

    METHODS: A gender-matched case-control study was conducted in the largest public sector cardiac hospital of Pakistan, and the data of 460 subjects were collected. The dataset comprised of eight nonclinical features. Four supervised ML algorithms were used to train and test the models to predict the CVDs status by considering traditional logistic regression (LR) as the baseline model. The models were validated through the train-test split (70:30) and tenfold cross-validation approaches.

    RESULTS: Random forest (RF), a nonlinear ML algorithm, performed better than other ML algorithms and LR. The area under the curve (AUC) of RF was 0.851 and 0.853 in the train-test split and tenfold cross-validation approach, respectively. The nonclinical features yielded an admissible accuracy (minimum 71%) through the LR and ML models, exhibiting its predictive capability in risk estimation.

    CONCLUSION: The satisfactory performance of nonclinical features reveals that these features and flexible computational methodologies can reinforce the existing risk prediction models for better healthcare services.

  2. Fulltext Graphene Oxide-Doped MgO Nanostructures for Highly Efficient Dye Degradation and Bactericidal Action
    Ikram M, Inayat T, Haider A, Ul-Hamid A, Haider J, Nabgan W, et al.
    Nanoscale Res Lett, 2021 Apr 07;16(1):56.
    PMID: 33825981 DOI: 10.1186/s11671-021-03516-z
    Various concentrations (0.01, 0.03 and 0.05 wt ratios) of graphene oxide (GO) nanosheets were doped into magnesium oxide (MgO) nanostructures using chemical precipitation technique. The objective was to study the effect of GO dopant concentrations on the catalytic and antibacterial behavior of fixed amount of MgO. XRD technique revealed cubic phase of MgO, while its crystalline nature was confirmed through SAED profiles. Functional groups presence and Mg-O (443 cm-1) in fingerprint region was evident with FTIR spectroscopy. Optical properties were recorded via UV-visible spectroscopy with redshift pointing to a decrease in band gap energy from 5.0 to 4.8 eV upon doping. Electron-hole recombination behavior was examined through photoluminescence (PL) spectroscopy. Raman spectra exhibited D band (1338 cm-1) and G band (1598 cm-1) evident to GO doping. Formation of nanostructure with cubic and hexagon morphology was confirmed with TEM, whereas interlayer average d-spacing of 0.23 nm was assessed using HR-TEM. Dopants existence and evaluation of elemental constitution Mg, O were corroborated using EDS technique. Catalytic activity against methyl blue ciprofloxacin (MBCF) was significantly reduced (45%) for higher GO dopant concentration (0.05), whereas bactericidal activity of MgO against E. coli was improved significantly (4.85 mm inhibition zone) upon doping with higher concentration (0.05) of GO, owing to the formation of nanorods.
  3. Fulltext Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches
    Sajid MR, Almehmadi BA, Sami W, Alzahrani MK, Muhammad N, Chesneau C, et al.
    Int J Environ Res Public Health, 2021 11 29;18(23).
    PMID: 34886312 DOI: 10.3390/ijerph182312586
    Criticism of the implementation of existing risk prediction models (RPMs) for cardiovascular diseases (CVDs) in new populations motivates researchers to develop regional models. The predominant usage of laboratory features in these RPMs is also causing reproducibility issues in low-middle-income countries (LMICs). Further, conventional logistic regression analysis (LRA) does not consider non-linear associations and interaction terms in developing these RPMs, which might oversimplify the phenomenon. This study aims to develop alternative machine learning (ML)-based RPMs that may perform better at predicting CVD status using nonlaboratory features in comparison to conventional RPMs. The data was based on a case-control study conducted at the Punjab Institute of Cardiology, Pakistan. Data from 460 subjects, aged between 30 and 76 years, with (1:1) gender-based matching, was collected. We tested various ML models to identify the best model/models considering LRA as a baseline RPM. An artificial neural network and a linear support vector machine outperformed the conventional RPM in the majority of performance matrices. The predictive accuracies of the best performed ML-based RPMs were between 80.86 and 81.09% and were found to be higher than 79.56% for the baseline RPM. The discriminating capabilities of the ML-based RPMs were also comparable to baseline RPMs. Further, ML-based RPMs identified substantially different orders of features as compared to baseline RPM. This study concludes that nonlaboratory feature-based RPMs can be a good choice for early risk assessment of CVDs in LMICs. ML-based RPMs can identify better order of features as compared to the conventional approach, which subsequently provided models with improved prognostic capabilities.
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