Dynamic Recommendation: Disease Prediction and Prevention Using Recommender System
Background: In today’s world, chronic diseases are predominant health problems and cause heavy burden on society; therefore early diagnosis and even prediction of the disease is a way to reduce this burden. In this project, we tried to use recommender system to predict which other diseases a chronic...
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Zabol University of Medical sciences
2016-06-01
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Online Access: | http://ijbsm.zbmu.ac.ir/PDF/IJBSM-1-13.pdf |
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doaj-bb3a5409e60d4603982e09a12905f59e2020-11-24T22:43:20ZengZabol University of Medical sciencesInternational Journal of Basic Science in Medicine2476-664X2016-06-0111131710.15171/ijbsm.2016.04IJBSM-1-20160701030239Dynamic Recommendation: Disease Prediction and Prevention Using Recommender SystemMahdi Nasiri0Behrouz Minaei1Amir KianiComputer Engineering Department, Iran University of Science and Technology (IUST), Tehran, IranDepartment of Mathematics and Computer Science, Amir Kabir University of Technology, Tehran, IranBackground: In today’s world, chronic diseases are predominant health problems and cause heavy burden on society; therefore early diagnosis and even prediction of the disease is a way to reduce this burden. In this project, we tried to use recommender system to predict which other diseases a chronic patient is susceptible for. Methods: In this study, through a dynamic recommender system, we evaluated patients’ treatment destiny during the time. Results: It was shown that our method increased accuracy and reduced error compared with other recommendation methods in disease prediction. Conclusion: Compared to current usual methods, in our method we used previous patients’ characteristics as one of the factorization variables to predict destiny of future patients. Furthermore, using this method, we can predict which complication or disease the patient would suffer from first in future. Therefore, we can manage policies toward disease burden reduction by implementing prevention programs.http://ijbsm.zbmu.ac.ir/PDF/IJBSM-1-13.pdfRecommender systemDisease predictionCollaborative filteringData miningTreatment |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mahdi Nasiri Behrouz Minaei Amir Kiani |
spellingShingle |
Mahdi Nasiri Behrouz Minaei Amir Kiani Dynamic Recommendation: Disease Prediction and Prevention Using Recommender System International Journal of Basic Science in Medicine Recommender system Disease prediction Collaborative filtering Data mining Treatment |
author_facet |
Mahdi Nasiri Behrouz Minaei Amir Kiani |
author_sort |
Mahdi Nasiri |
title |
Dynamic Recommendation: Disease Prediction and Prevention Using Recommender System |
title_short |
Dynamic Recommendation: Disease Prediction and Prevention Using Recommender System |
title_full |
Dynamic Recommendation: Disease Prediction and Prevention Using Recommender System |
title_fullStr |
Dynamic Recommendation: Disease Prediction and Prevention Using Recommender System |
title_full_unstemmed |
Dynamic Recommendation: Disease Prediction and Prevention Using Recommender System |
title_sort |
dynamic recommendation: disease prediction and prevention using recommender system |
publisher |
Zabol University of Medical sciences |
series |
International Journal of Basic Science in Medicine |
issn |
2476-664X |
publishDate |
2016-06-01 |
description |
Background: In today’s world, chronic diseases are predominant health problems and cause heavy burden on society; therefore early diagnosis and even prediction of the disease is a way to reduce this burden. In this project, we tried to use recommender system to predict which other diseases a chronic patient is susceptible for. Methods: In this study, through a dynamic recommender system, we evaluated patients’ treatment destiny during the time. Results: It was shown that our method increased accuracy and reduced error compared with other recommendation methods in disease prediction. Conclusion: Compared to current usual methods, in our method we used previous patients’ characteristics as one of the factorization variables to predict destiny of future patients. Furthermore, using this method, we can predict which complication or disease the patient would suffer from first in future. Therefore, we can manage policies toward disease burden reduction by implementing prevention programs. |
topic |
Recommender system Disease prediction Collaborative filtering Data mining Treatment |
url |
http://ijbsm.zbmu.ac.ir/PDF/IJBSM-1-13.pdf |
work_keys_str_mv |
AT mahdinasiri dynamicrecommendationdiseasepredictionandpreventionusingrecommendersystem AT behrouzminaei dynamicrecommendationdiseasepredictionandpreventionusingrecommendersystem AT amirkiani dynamicrecommendationdiseasepredictionandpreventionusingrecommendersystem |
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1725696319083773952 |