[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122027-en":3,"doc-seo-122027-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122027,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Machine learning based tuberculosis (ML-TB) health predictor model - early TB health disease prediction with ML models for prevention in developing countries","Tuberculosis (TB) remains a leading infectious cause of death in developing countries, and early prevention is critical. This study proposes a prototype early-TB health prediction approach using machine learning algorithms driven by primary symptoms, signs, and risk factors, supported by a unique patient dataset collected from three top-ranked hospitals in Sindh, Pakistan. Using 1,200 survey patient-records from ICT Kotri, LUMHS Jamshoro, and Civil Hospital Hyderabad, the work evaluates five benchmark models with standard performance metrics. Results show high accuracy across decision tree, naive Bayes, logistic regression, AdaBoost, and neural network models, enabling effective early TB diagnosis.","Submitted 27 June 2024  \nAccepted 18 September 2024 Published 16 October 2024  \nCorresponding author Aftab Ahmed Chandio, [chandio.aftab@usindh.edu.pk](chandio.aftab@usindh.edu.pk)  \nAcademic editor Jiayan Zhou  \nAdditional Information and Declarations can be found on page 17  \nDOI 10.7717/peerj-cs.2397  \nCopyright 2024 Karmani et al.  \nDistributed under  \nCreative Commons CC-BY 4.0  \nMachine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention in developing countries  \nPriyanka Karmani 1, Aftab Ahmed Chandio 1, Imtiaz Ali Korejo 1, Oluwarotimi Williams Samuel2 and Majed Aborokbah3  \n1 Institute of Mathematics and Computer Science, University of Sindh, Jamshoro, Sindh, Pakistan  \n2 School of Computing and Data Science Research Center, University of Derby, Derby, United Kingdom  \n3 Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia  \nABSTRACT  \nBackground: Tuberculosis (TB) remains one of the top infectious killers in the world and a prominent fatal disease in developing countries. This study proposes a prototypical solution to early prevention of TB based on its primary symptoms, signs, and risk factors, implemented by means of machine learning (ML) predictive algorithms. Further novelty of the study lies in the uniqueness of patient dataset collected from three top-ranked hospitals of Sindh, Pakistan, via a self-administered survey patient-records that comprises a set of questions asked by the doctors treating TB patients in real-time. A total of 1,200 survey patient-records were evenly distributed among all three hospitals, viz. ICT Kotri, LUMHS Jamshoro, and Civil Hospital Hyderabad.  \nMethods: To develop the required prototypes, the research made use of five distinct benchmark ML algorithms: decision tree (DT), Gaussian naive Bayes (GNB), logistic regression classifier (LRC), adaptive boosting (AdaBoost), and neural network (NN), whose performance was evaluated by considering various performance metrics, i.e., accuracy, precision, recall, F1 score, and confusion matrix.  \nResults: The experimental results, graphically visualized and systematically discoursed, demonstrate that early detection of TB classifiers, including DT, GNB, LRC, AdaBoost, and NN, attained accuracy rates of 92.11%, 89.04%, 90.35%, 93.42%, and 92.98%, respectively. These results indicate effective diagnosis of TB disease by each implemented ML algorithm.  \nSubjects Bioinformatics, Artificial Intelligence, Data Mining and Machine Learning, Emerging Technologies  \nKeywords Tuberculosis (TB) diagnosis, Machine learning (ML), Good health and well-being, Optimal ML model for TB diagnosis  \nINTRODUCTION  \nAmong many toxic diseases, one of the leading fatal diseases is tuberculosis (TB), defined as,“an infectious disease caused by bacillus Mycobacterium tuberculosis”. TB primarily communicates a disease to human respiratory tract i.e., Lungs. According to World Health Organization (WHO), Pakistan ranks fifth among the 30 high TB-burden countries  \nHow to cite this article Karmani P, Chandio AA, Korejo IA, Samuel OW, Aborokbah M. 2024. Machine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention in developing countries. PeerJ Comput.  \nSci. 10:e2397 DOI 10.7717/peerj-cs.2397  \nglobally (The World Health Organization, 2024a) . To get control of this lethal disease and reduce death ratio, ensuring good health and well-being, it is essential to put forward an automated solution for TB diagnosis in its initial phase. In this contemporary era, the integration of cutting-edge technologies in the field of Medical and Health sciences, called Healthcare Informatics (HI), attempts to originate innovative and digital solutions as well as novel apparatuses to save human lives in an effective manner. Consequently, current research carried out a study on an automated machine learning (ML) oriented solu","cbCaitSxMh129j2q","https://ap.wps.com/l/cbCaitSxMh129j2q","pdf",3832018,1,20,"English","en",105,"# Abstract\n# Introduction\n## Healthcare Informatics (HI) and Machine Learning (ML)\n## The Targeted Disease: Tuberculosis (TB)\n## Literature Review\n## ML-TB Predictor\n## Data Analysis\n## Results and Discussion\n## Conclusions","[{\"question\":\"What problem does the ML-TB health predictor model address?\",\"answer\":\"It targets early TB health disease prediction and prevention by leveraging primary symptoms, signs, and risk factors to support timely diagnosis in high-burden developing settings.\"},{\"question\":\"How was the dataset for the study collected?\",\"answer\":\"A total of 1,200 survey patient-records were gathered evenly from three hospitals in Sindh, Pakistan, using a self-administered questionnaire with real-time doctor-recorded questions for TB patients.\"},{\"question\":\"Which machine learning models were evaluated and how well did they perform?\",\"answer\":\"Five benchmark models—decision tree, Gaussian naive Bayes, logistic regression, AdaBoost, and neural network—were tested using metrics such as accuracy, precision, recall, F1 score, and confusion matrix. Reported accuracy rates range from about 89.04% to 93.42% across the models.\"}]","Machine learning based tuberculosis (ML-TB) health predictor model - early TB health disease prediction with ML models for prevention in developing countries | PDF",1785808353,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-tuberculosis-ml-tb-health-predictor-model-early-tb-health-disease-prediction-with-ml-models-for-prevention-in-developing-countries","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-tuberculosis-ml-tb-health-predictor-model-early-tb-health-disease-prediction-with-ml-models-for-prevention-in-developing-countries/122027/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the ML-TB health predictor model address?","Question",{"text":75,"@type":76},"It targets early TB health disease prediction and prevention by leveraging primary symptoms, signs, and risk factors to support timely diagnosis in high-burden developing settings.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset for the study collected?",{"text":80,"@type":76},"A total of 1,200 survey patient-records were gathered evenly from three hospitals in Sindh, Pakistan, using a self-administered questionnaire with real-time doctor-recorded questions for TB patients.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models were evaluated and how well did they perform?",{"text":84,"@type":76},"Five benchmark models—decision tree, Gaussian naive Bayes, logistic regression, AdaBoost, and neural network—were tested using metrics such as accuracy, precision, recall, F1 score, and confusion matrix. 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