Displaying publications 1 - 20 of 24 in total

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  1. RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system
    Saybani MR, Shamshirband S, Golzari S, Wah TY, Saeed A, Mat Kiah ML, et al.
    Med Biol Eng Comput, 2016 Mar;54(2-3):385-99.
    PMID: 26081904 DOI: 10.1007/s11517-015-1323-6
    Tuberculosis is a major global health problem that has been ranked as the second leading cause of death from an infectious disease worldwide, after the human immunodeficiency virus. Diagnosis based on cultured specimens is the reference standard; however, results take weeks to obtain. Slow and insensitive diagnostic methods hampered the global control of tuberculosis, and scientists are looking for early detection strategies, which remain the foundation of tuberculosis control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose the disease. The objective of this study is to diagnose tuberculosis using a machine learning method. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. In order to increase the classification accuracy, this study introduces a new hybrid system that incorporates real tournament selection mechanism into the AIRS. This mechanism is used to control the population size of the model and to overcome the existing selection pressure. Patient epacris reports obtained from the Pasteur laboratory in northern Iran were used as the benchmark data set. The sample consisted of 175 records, from which 114 (65 %) were positive for TB, and the remaining 61 (35 %) were negative. The classification performance was measured through tenfold cross-validation, root-mean-square error, sensitivity, and specificity. With an accuracy of 100 %, RMSE of 0, sensitivity of 100 %, and specificity of 100 %, the proposed method was able to successfully classify tuberculosis cases. In addition, the proposed method is comparable with top classifiers used in this research.
    Matched MeSH terms: Expert Systems*
  2. Health expert's tacit knowledge acquisition and representation using specialised healthcare scenarios
    Cheah YN, Abidi SS
    Stud Health Technol Inform, 2000;77:837-41.
    PMID: 11187672
    The abundance and transient nature to healthcare knowledge has rendered it difficult to acquire with traditional knowledge acquisition methods. In this paper, we propose a Knowledge Management approach, through the use of scenarios, as a mean to acquire and represent tacit healthcare knowledge. This proposition is based on the premise that tacit knowledge is best manifested in atypical situations. We also provide an overview of the representational scheme and novel acquisition mechanism of scenarios.
    Matched MeSH terms: Expert Systems*
  3. Fulltext Development of a decision support system for analysis and solutions of prolonged standing in the workplace
    Halim I, Arep H, Kamat SR, Abdullah R, Omar AR, Ismail AR
    Saf Health Work, 2014 Jun;5(2):97-105.
    PMID: 25180141 DOI: 10.1016/j.shaw.2014.04.002
    BACKGROUND: Prolonged standing has been hypothesized as a vital contributor to discomfort and muscle fatigue in the workplace. The objective of this study was to develop a decision support system that could provide systematic analysis and solutions to minimize the discomfort and muscle fatigue associated with prolonged standing.

    METHODS: The integration of object-oriented programming and a Model Oriented Simultaneous Engineering System were used to design the architecture of the decision support system.

    RESULTS: Validation of the decision support system was carried out in two manufacturing companies. The validation process showed that the decision support system produced reliable results.

    CONCLUSION: The decision support system is a reliable advisory tool for providing analysis and solutions to problems related to the discomfort and muscle fatigue associated with prolonged standing. Further testing of the decision support system is suggested before it is used commercially.

    Matched MeSH terms: Expert Systems
  4. Fulltext Paediatric orthopaedic expert system
    Lau CF, Malek S, Gunalan R, Saw A, Milow P, Song C
    Health Informatics J, 2023;29(4):14604582231218530.
    PMID: 38019888 DOI: 10.1177/14604582231218530
    The paediatric orthopaedic expert system analyses and predicts the healing time of limb fractures in children using machine learning. As far we know, no published research on the paediatric orthopaedic expert system that predicts paediatric fracture healing time using machine learning has been published. The University Malaya Medical Centre (UMMC) offers paediatric orthopaedic data, comprises children under the age of 12 radiographs limb fractures with ages recorded from the date and time of initial trauma. SVR algorithms are used to predict and discover variables associated with fracture healing time. This study developed an expert system capable of predicting healing time, which can assist general practitioners and healthcare practitioners during treatment and follow-up. The system is available online at https://kidsfractureexpert.com/.
    Matched MeSH terms: Expert Systems
  5. Development of expert system for biobased polymer material selection: food packaging application
    Sanyang ML, Sapuan SM
    J Food Sci Technol, 2015 Oct;52(10):6445-54.
    PMID: 26396389 DOI: 10.1007/s13197-015-1759-6
    Biobased food packaging materials are gaining more attention owing to their intrinsic biodegradable nature and renewability. Selection of suitable biobased polymers for food packaging applications could be a tedious task with potential mistakes in choosing the best materials. In this paper, an expert system was developed using Exsys Corvid software to select suitable biobased polymer materials for packaging fruits, dry food and dairy products. If - Then rule based system was utilized to accomplish the material selection process whereas a score system was formulated to facilitate the ranking of selected materials. The expert system selected materials that satisfied all constraints and selection results were presented in suitability sequence depending on their scores. The expert system selected polylactic acid (PLA) as the most suitable material.
    Matched MeSH terms: Expert Systems
  6. Secondary triage classification using an ensemble random forest technique
    Azeez D, Gan KB, Mohd Ali MA, Ismail MS
    Technol Health Care, 2015;23(4):419-28.
    PMID: 25791174 DOI: 10.3233/THC-150907
    BACKGROUND: Triage of patients in the emergency department is a complex task based on several uncertainties and ambiguous information. Triage must be implemented within two to five minutes to avoid potential fatality and increased waiting time.
    OBJECTIVE: An intelligent triage system has been proposed for use in a triage environment to reduce human error.
    METHODS: This system was developed based on the objective primary triage scale (OPTS) that is currently used in the Universiti Kebangsaan Malaysia Medical Center. Both primary and secondary triage models are required to develop this system. The primary triage model has been reported previously; this work focused on secondary triage modelling using an ensemble random forest technique. The randomized resampling method was proposed to balance the data unbalance prior to model development.
    RESULTS: The results showed that the 300% resampling gave a low out-of-bag error of 0.02 compared to 0.37 without pre-processing. This model has a sensitivity and specificity of 0.98 and 0.89, respectively, for the unseen data.
    CONCLUSION: With this combination, the random forest reduces the variance, and the randomized resembling reduces the bias, leading to the reduced out-of-bag error.
    KEYWORDS: Decision support system; emergency department; random forest; randomized resampling
    Matched MeSH terms: Expert Systems
  7. Automatic diagnosis of tuberculosis disease based on Plasmonic ELISA and color-based image classification
    AbuHassan KJ, Bakhori NM, Kusnin N, Azmi UZM, Tania MH, Evans BA, et al.
    Annu Int Conf IEEE Eng Med Biol Soc, 2017 Jul;2017:4512-4515.
    PMID: 29060900 DOI: 10.1109/EMBC.2017.8037859
    Tuberculosis (TB) remains one of the most devastating infectious diseases and its treatment efficiency is majorly influenced by the stage at which infection with the TB bacterium is diagnosed. The available methods for TB diagnosis are either time consuming, costly or not efficient. This study employs a signal generation mechanism for biosensing, known as Plasmonic ELISA, and computational intelligence to facilitate automatic diagnosis of TB. Plasmonic ELISA enables the detection of a few molecules of analyte by the incorporation of smart nanomaterials for better sensitivity of the developed detection system. The computational system uses k-means clustering and thresholding for image segmentation. This paper presents the results of the classification performance of the Plasmonic ELISA imaging data by using various types of classifiers. The five-fold cross-validation results show high accuracy rate (>97%) in classifying TB images using the entire data set. Future work will focus on developing an intelligent mobile-enabled expert system to diagnose TB in real-time. The intelligent system will be clinically validated and tested in collaboration with healthcare providers in Malaysia.
    Matched MeSH terms: Expert Systems
  8. Fulltext EXPERT SYSTEM TO DIAGNOSE DENGUE FEVER
    Mohd Zulhilmi Aqil Muhamad, Noor Hafhizah Abd Rahim
    UMT Journal of Undergraduate Research, 2019;1(4):67-76.
    MyJurnal
    Expert system is a system that emulates experts to aid in decision making. This system can be applied in various categories such as diagnosis, prediction, interpretation, and others. Expert System to Diagnose Dengue Fever is a web-based system which is integrated with prolog language in order to provide rules for dengue fever detection. The aims of this research are to study dengue fever symptoms and other illnesses related to the fever, to design an inference engine, and to build an expert system. The challenges faced while developing this expert system were the complexity of prolog codes and their integration with the web development. In this system, rules were developed by prolog language which define dengue fever and accuracy based on input from the user. This system is expected to aid users in self-detecting early symptoms of dengue fever before seeing the doctors.
    Matched MeSH terms: Expert Systems
  9. Fulltext Development of E-ACTIVETRANS for young professional planners/engineers
    Bibie Sara Salleh, Jasni Md Noor, Riza Atiq O.K Rahmat, Amiruddin Ismail
    Pertanika Journal of Science & Technology, 2017;25(105):211-216.
    MyJurnal
    This paper presents a development of an expert system to be used as an advisory in finding the solution to problems which are normally solved by human experts. The E-ACTIVETRANS is developed to help young engineers/planners in designing a new cycle lane in urban areas and also to help in reallocation of an existing roadway space for cycle lanes. This system has three sub-systems: Planning on Strategies to Shift from Passive Transportation to Active Transportation, Design on Bicycle Facilities and Examples of Successful Implementation. This paper focuses on the design of bicycle facilities whereby the prototype was developed based on data acquired from the domain experts who are involved in bicycle facility module design, as well as the initial text analysis obtained during the domain familiarisation stage. The validation of the system was performed through a comparison of knowledge content in E-ACTIVETRANS based on expert opinion. The average level of acceptance is 91 percent which validates the system and knowledge of the experts.
    Matched MeSH terms: Expert Systems
  10. Fulltext Geospatial Tuberculosis Information System for Airborne Disease Management
    Abdul Rauf Abdul Rasam, Noresah Mohd Shariff, Dony, Jiloris F., Saiful Aman Sulaiman
    Jurnal Inovasi Malaysia, 2018;2(1):75-88.
    MyJurnal
    An innovative health information system can be used to support the control of tuberculosis (TB) in Malaysia. The existing system of MyTB has helped in the national TB information management and decision-making process. However, the system can be further enhanced by producing a prototype of Geospatial Tuberculosis Information System (GeoTBiS). It is a geospatial decision support system that was initially proposed in Shah Alam, Selangor. Geospatial data has spatio-temporal characteristics that can be used to understand the basic elements of TB aetiology, while geospatial operations are employed to collect, manage and disseminate the data in a geographical information system (GIS) environment. The disease map and epidemiological risk analysis are produced using a global positioning system (GPS), satellite imagery, geostatistical analysis and web mapping services. This GeoTBiS has demonstrated the geospatial capabilities in enhancing the current system functions, and several recommendations towards a practicable application.
    Matched MeSH terms: Expert Systems
  11. Scoring systems, expert assessment, and identification of risk factors for the emergence of delirium in paediatric patients: Prospective cohort study
    Sari D, Widyastuti Y, Hendy HH, Dharma IA, Pancarani A, Krislee A
    Med J Malaysia, 2024 Mar;79(2):151-156.
    PMID: 38553919
    INTRODUCTION: Emergence delirium (ED) is a transient irritative and dissociative state that arises after the cessation of anaesthesia in patients who do not respond to calming measures. There are many risk factors for ED, but the exact cause and underlying mechanism have not been determined because the definition of ED is still unclear in consensus. This study aims to determine ED incidence, identify ED risk factors and external validation of Watcha, Cravero and expert assessment to Pediatric Anesthesia Emergence Delirium (PAED) scoring system in ED prediction.

    MATERIALS AND METHODS: This study is a prospective cohort study on 79 paediatrics who underwent elective surgery with general anaesthesia. Parameter measures include the incidence of ED, ED risk factors, and the relationship between PAED, Watcha, Cravero score and expert assessment. The ED risk factor was analysed using univariate and multivariate analysis. The relationship between PAED, Watcha, Cravero score, and expert assessment was determined using Receiver Operating Characteristic (ROC) curve analysis.

    RESULTS: The incidence of ED was 22.8%. All parameters examined in this study showed p < 0.05. Watcha's scoring correlates with the PAED scoring and shows the highest discrimination ability with AUC 0.741 and p < 0.05.

    CONCLUSION: The incidence of ED in paediatrics is relatively high. Compared to others, Watcha score are more reliable for ED prediction. However, some demographic and perioperative factors are not the risk factor of ED.

    Matched MeSH terms: Expert Systems
  12. Fulltext Developing a web-based advisory expert system for implementing traffic calming strategies
    Falamarzi A, Borhan MN, Rahmat RA
    ScientificWorldJournal, 2014;2014:757981.
    PMID: 25276861 DOI: 10.1155/2014/757981
    Lack of traffic safety has become a serious issue in residential areas. In this paper, a web-based advisory expert system for the purpose of applying traffic calming strategies on residential streets is described because there currently lacks a structured framework for the implementation of such strategies. Developing an expert system can assist and advise engineers for dealing with traffic safety problems. This expert system is developed to fill the gap between the traffic safety experts and people who seek to employ traffic calming strategies including decision makers, engineers, and students. In order to build the expert system, examining sources related to traffic calming studies as well as interviewing with domain experts have been carried out. The system includes above 150 rules and 200 images for different types of measures. The system has three main functions including classifying traffic calming measures, prioritizing traffic calming strategies, and presenting solutions for different traffic safety problems. Verifying, validating processes, and comparing the system with similar works have shown that the system is consistent and acceptable for practical uses. Finally, some recommendations for improving the system are presented.
    Matched MeSH terms: Expert Systems*
  13. Fulltext Healthcare knowledge management through building and operationalising healthcare enterprise memory
    Cheah YN, Abidi SS
    Stud Health Technol Inform, 1999;68:726-30.
    PMID: 10724990
    In this paper we suggest that the healthcare enterprise needs to be more conscious of its vast knowledge resources vis-à-vis the exploitation of knowledge management techniques to efficiently manage its knowledge. The development of healthcare enterprise memory is suggested as a solution, together with a novel approach advocating the operationalisation of healthcare enterprise memories leading to the modelling of healthcare processes for strategic planning. As an example, we present a simulation of Service Delivery Time in a hospital's OPD.
    Matched MeSH terms: Expert Systems*
  14. TIDE: an intelligent home-based healthcare information & diagnostic environment
    Abidi SS
    Stud Health Technol Inform, 1999;68:720-5.
    PMID: 10724989
    The 21st century promises to usher in an era of Internet based healthcare services--Tele-Healthcare. Such services augur well with the on-going paradigm shift in healthcare delivery patterns, i.e. patient centred services as opposed to provider centred services and wellness maintenance as opposed to illness management. This paper presents a Tele-Healthcare info-structure TIDE--an 'intelligent' wellness-oriented healthcare delivery environment. TIDE incorporates two WWW-based healthcare systems: (1) AIMS (Automated Health Monitoring System) for wellness maintenance and (2) IDEAS (Illness Diagnostic & Advisory System) for illness management. Our proposal comes from an attempt to rethink the sources of possible leverage in improving healthcare; vis-à-vis the provision of a continuum of personalised home-based healthcare services that emphasise the role of the individual in self health maintenance.
    Matched MeSH terms: Expert Systems*
  15. Applying data mining in healthcare: an info-structure for delivering 'data-driven' strategic services
    Abidi SS
    Stud Health Technol Inform, 1999;68:453-6.
    PMID: 10724926
    Presently, there is a growing demand from the healthcare community to leverage upon and transform the vast quantities of healthcare data into value-added, 'decision-quality' knowledge, vis-à-vis, strategic knowledge services oriented towards healthcare management and planning. To meet this end, we present a Strategic Knowledge Services Info-structure that leverages on existing healthcare knowledge/data bases to derive decision-quality knowledge-knowledge that is extracted from healthcare data through services akin to knowledge discovery in databases and data mining.
    Matched MeSH terms: Expert Systems*
  16. A proposed hierarchical fuzzy inference system for the diagnosis of arthritic diseases
    Lim CK, Yew KM, Ng KH, Abdullah BJ
    Australas Phys Eng Sci Med, 2002 Sep;25(3):144-50.
    PMID: 12416592 DOI: 10.1007/BF03178776
    Development of computer-based medical inference systems is always confronted with some difficulties. In this paper, difficulties of designing an inference system for the diagnosis of arthritic diseases are described, including variations of disease manifestations under various situations and conditions. Furthermore, the need for a huge knowledge base would result in low efficiency of the inference system. We proposed a hierarchical model of the fuzzy inference system as a possible solution. With such a model, the diagnostic process is divided into two levels. The first level of the diagnosis reduces the scope of diagnosis to be processed by the second level. This will reduce the amount of input and mapping for the whole diagnostic process. Fuzzy relational theory is the core of this system and it is used in both levels to improve the accuracy.
    Matched MeSH terms: Expert Systems*
  17. Development and evaluation of a computer expert system for the management of fetal distress
    Wong KK, Ng KH, Nah SH, Yusof K, Rajeswari K
    Asia Oceania J Obstet Gynaecol, 1994 Mar;20(1):19-23.
    PMID: 8172522
    The general lack of specialist obstetricians in a developing country such as Malaysia prompted us to develop a computer expert system for the management of fetal distress in rural hospitals. It was based on accepted production rules and implemented on a microcomputer. The clinical prototype was evaluated by 8 specialist obstetricians and 21 non-specialist doctors involved in obstetric care. The initial impression was that this type of expert system may help in diagnosis, decision-making and teaching.
    Matched MeSH terms: Expert Systems*
  18. Healthcare knowledge acquisition: an ontology-based approach using the extensible markup language (XML)
    Cheah YN, Abidi SS
    Stud Health Technol Inform, 2000;77:827-31.
    PMID: 11187669
    The healthcare enterprise requires a great deal of knowledge to maintain premium efficiency in the delivery of quality healthcare. We employ Knowledge Management based knowledge acquisition strategies to procure 'tacit' healthcare knowledge from experienced healthcare practitioners. Situational, problem-specific Scenarios are proposed as viable knowledge acquisition and representation constructs. We present a healthcare Tacit Knowledge Acquisition Info-structure (TKAI) that allows remote healthcare practitioners to record their tacit knowledge. TKAI employs (a) ontologies for standardisation of tacit knowledge and (b) XML to represent scenario instances for their transfer over the Internet to the server-side Scenario-Base and for the global sharing of acquired tacit healthcare knowledge.
    Matched MeSH terms: Expert Systems
  19. Fulltext An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification
    Siddiqui MF, Reza AW, Kanesan J
    PLoS One, 2015;10(8):e0135875.
    PMID: 26280918 DOI: 10.1371/journal.pone.0135875
    A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.
    Matched MeSH terms: Expert Systems/instrumentation*
  20. A decision support system for automatic screening of non-proliferative diabetic retinopathy
    Reza AW, Eswaran C
    J Med Syst, 2011 Feb;35(1):17-24.
    PMID: 20703589 DOI: 10.1007/s10916-009-9337-y
    The increasing number of diabetic retinopathy (DR) cases world wide demands the development of an automated decision support system for quick and cost-effective screening of DR. We present an automatic screening system for detecting the early stage of DR, which is known as non-proliferative diabetic retinopathy (NPDR). The proposed system involves processing of fundus images for extraction of abnormal signs, such as hard exudates, cotton wool spots, and large plaque of hard exudates. A rule based classifier is used for classifying the DR into two classes, namely, normal and abnormal. The abnormal NPDR is further classified into three levels, namely, mild, moderate, and severe. To evaluate the performance of the proposed decision support framework, the algorithms have been tested on the images of STARE database. The results obtained from this study show that the proposed system can detect the bright lesions with an average accuracy of about 97%. The study further shows promising results in classifying the bright lesions correctly according to NPDR severity levels.
    Matched MeSH terms: Expert Systems
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