research-article

Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1681 - 1690

https://doi.org/10.1145/2783258.2788587

Published: 10 August 2015 Publication History

Abstract

Non-communicable diseases (NCDs) are no longer just a problem for high-income countries, but they are also a problem that affects developing countries. Preventive medicine is definitely the key to combat NCDs; however, the cost of preventive programs is a critical issue affecting the popularization of these medicine programs in developing countries. In this study, we investigate predictive modeling for providing a low-cost preventive medicine program. In our two-year-long field study in Bangladesh, we collected the health checkup results of 15,075 subjects, the data of 6,607 prescriptions, and the follow-up examination results of 2,109 subjects. We address three prediction problems, namely subject risk prediction, drug recommendation, and future risk prediction, by using machine learning techniques; our multiple-classifier approach successfully reduced the costs of health checkups, a multi-task learning method provided accurate recommendation for specific types of drugs, and an active learning method achieved an efficient assignment of healthcare workers for the follow-up care of subjects.

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Cited By

Alam MSajib MRahman FEther SHanson MSayeed AAkter ENusrat NIslam TRaza STanvir KChisti MRahman QHossain ALayek MZaman ARana JRahman SArifeen SRahman AAhmed A(2024)Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping ReviewJournal of Medical Internet Research10.2196/5471026(e54710)Online publication date: 28-Oct-2024
https://doi.org/10.2196/54710
Helgheim BMaia RFerreira JMartins A(2019)Merging Data Diversity of Clinical Medical Records to Improve EffectivenessInternational Journal of Environmental Research and Public Health10.3390/ijerph1605076916:5(769)Online publication date: 3-Mar-2019
https://doi.org/10.3390/ijerph16050769
De-Arteaga MHerlands WNeill DDubrawski A(2018)Machine Learning for the Developing WorldACM Transactions on Management Information Systems10.1145/32105489:2(1-14)Online publication date: 24-Aug-2018
https://dl.acm.org/doi/10.1145/3210548
Show More Cited By

Index Terms

Predictive Approaches for Low-Cost Preventive Medicine Program in Developing Countries
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
    2. Health informatics
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
,
Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
,
Graham Williams
Australian Taxation Office

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 10 August 2015

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KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

NSW, Sydney, Australia

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KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Alam MSajib MRahman FEther SHanson MSayeed AAkter ENusrat NIslam TRaza STanvir KChisti MRahman QHossain ALayek MZaman ARana JRahman SArifeen SRahman AAhmed A(2024)Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping ReviewJournal of Medical Internet Research10.2196/5471026(e54710)Online publication date: 28-Oct-2024
https://doi.org/10.2196/54710
Helgheim BMaia RFerreira JMartins A(2019)Merging Data Diversity of Clinical Medical Records to Improve EffectivenessInternational Journal of Environmental Research and Public Health10.3390/ijerph1605076916:5(769)Online publication date: 3-Mar-2019
https://doi.org/10.3390/ijerph16050769
De-Arteaga MHerlands WNeill DDubrawski A(2018)Machine Learning for the Developing WorldACM Transactions on Management Information Systems10.1145/32105489:2(1-14)Online publication date: 24-Aug-2018
https://dl.acm.org/doi/10.1145/3210548
KITSUREGAWA M(2017)Data Is Key to Societal Problem Solving「データこそ社会課題解決のための要」と信じて早30年TRENDS IN THE SCIENCES10.5363/tits.22.12_3422:12(12_34-12_39)Online publication date: 1-Dec-2017
https://doi.org/10.5363/tits.22.12_34
Sun WCai ZLiu FFang SWang G(2017)A survey of data mining technology on electronic medical records2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom)10.1109/HealthCom.2017.8210774(1-6)Online publication date: Oct-2017
https://doi.org/10.1109/HealthCom.2017.8210774
Jörger PBaba YKashima H(2016)Learning to EnumerateArtificial Neural Networks and Machine Learning – ICANN 201610.1007/978-3-319-44778-0_53(453-460)Online publication date: 13-Aug-2016
https://doi.org/10.1007/978-3-319-44778-0_53

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