{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/156762"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/156762","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Developing a Machine Learning Based Automated Screening Tool to Diagnose Silent Heart Attacks in Resource-Constrained Settings","abstract":"Aging populations worldwide pose significant financial and social challenges for low- and middle-income countries, particularly in supporting elderly individuals with chronic illnesses. These challenges are exacerbated by a shortage of high-fidelity diagnostic technology. While point-of-care tests offer a low-cost and mobile solution to limited diagnostic access, they lack the precision of more expensive tests and specialized medical expertise. This thesis develops a supervised machine learning-based diagnostic tool for silent heart attacks using both point-of-care and gold standard data from elders in Tamil Nadu, India, as part of a proposed solution to bridge the diagnostic gap. The research explores whether point-of-care data can be used to reliably identify risk, transforming low-cost inputs into predictions with signal. We also investigate how to operationalize these predictions in a referral pipeline. The results demonstrate that single-lead ECG inputs can effectively detect signals indicative of silent heart attacks. Based on model predictions and a cost-benefit analysis, we suggest risk score thresholds for classifying high-risk individuals for whom it is cost-effective to refer to escalated care. Additionally, integrating single-lead and 12-lead ECG data enhances diagnostic accuracy and supports the development of an operational referral pipeline.","abstract_html":"Aging populations worldwide pose significant financial and social challenges for low- and middle-income countries, particularly in supporting elderly individuals with chronic illnesses. These challenges are exacerbated by a shortage of high-fidelity diagnostic technology. While point-of-care tests offer a low-cost and mobile solution to limited diagnostic access, they lack the precision of more expensive tests and specialized medical expertise. This thesis develops a supervised machine learning-based diagnostic tool for silent heart attacks using both point-of-care and gold standard data from elders in Tamil Nadu, India, as part of a proposed solution to bridge the diagnostic gap. The research explores whether point-of-care data can be used to reliably identify risk, transforming low-cost inputs into predictions with signal. We also investigate how to operationalize these predictions in a referral pipeline. The results demonstrate that single-lead ECG inputs can effectively detect signals indicative of silent heart attacks. Based on model predictions and a cost-benefit analysis, we suggest risk score thresholds for classifying high-risk individuals for whom it is cost-effective to refer to escalated care. Additionally, integrating single-lead and 12-lead ECG data enhances diagnostic accuracy and supports the development of an operational referral pipeline.","abstract_has_math":false,"creators":["Real, Karyn N."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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These challenges are exacerbated by a shortage of high-fidelity diagnostic technology. While point-of-care tests offer a low-cost and mobile solution to limited diagnostic access, they lack the precision of more expensive tests and specialized medical expertise. This thesis develops a supervised machine learning-based diagnostic tool for silent heart attacks using both point-of-care and gold standard data from elders in Tamil Nadu, India, as part of a proposed solution to bridge the diagnostic gap. The research explores whether point-of-care data can be used to reliably identify risk, transforming low-cost inputs into predictions with signal. We also investigate how to operationalize these predictions in a referral pipeline. The results demonstrate that single-lead ECG inputs can effectively detect signals indicative of silent heart attacks. Based on model predictions and a cost-benefit analysis, we suggest risk score thresholds for classifying high-risk individuals for whom it is cost-effective to refer to escalated care. Additionally, integrating single-lead and 12-lead ECG data enhances diagnostic accuracy and supports the development of an operational referral pipeline."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["MNG"]},{"key":"dc:title","label":"Title","values":["Developing a Machine Learning Based Automated Screening Tool to Diagnose Silent Heart Attacks in Resource-Constrained Settings"]}]}],"canonical_facts":{"dc:contributor.advisor":["Duŕo, Esther"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Real, Karyn N."],"dc:date.accessioned":["2024-09-16T13:47:35Z"],"dc:date.available":["2024-09-16T13:47:35Z"],"dc:date.issued":["2024-05"],"dc:description.abstract":["Aging populations worldwide pose significant financial and social challenges for low- and middle-income countries, particularly in supporting elderly individuals with chronic illnesses. These challenges are exacerbated by a shortage of high-fidelity diagnostic technology. While point-of-care tests offer a low-cost and mobile solution to limited diagnostic access, they lack the precision of more expensive tests and specialized medical expertise. This thesis develops a supervised machine learning-based diagnostic tool for silent heart attacks using both point-of-care and gold standard data from elders in Tamil Nadu, India, as part of a proposed solution to bridge the diagnostic gap. The research explores whether point-of-care data can be used to reliably identify risk, transforming low-cost inputs into predictions with signal. We also investigate how to operationalize these predictions in a referral pipeline. The results demonstrate that single-lead ECG inputs can effectively detect signals indicative of silent heart attacks. Based on model predictions and a cost-benefit analysis, we suggest risk score thresholds for classifying high-risk individuals for whom it is cost-effective to refer to escalated care. Additionally, integrating single-lead and 12-lead ECG data enhances diagnostic accuracy and supports the development of an operational referral pipeline."],"dc:description.degree":["MNG"],"dc:identifier.uri":["https://hdl.handle.net/1721.1/156762"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Developing a Machine Learning Based Automated Screening Tool to Diagnose Silent Heart Attacks in Resource-Constrained Settings"],"dc:type":["Thesis"],"thesis:degree_name":["Master"]},"updated_at":"2026-07-22T22:22:22Z"}