Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension

Early diseases prediction plays an important role for improving healthcare quality and can help individuals avoid dangerous health situations before it is too late. This paper proposes a disease prediction model (DPM) to provide an early prediction for type 2 diabetes and hypertension based on indiv...

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Main Authors:	Norma Latif Fitriyani, Muhammad Syafrudin, Ganjar Alfian, Jongtae Rhee
Format:	Article
Language:	English
Published:	IEEE 2019-01-01
Series:	IEEE Access
Subjects:	Diabetes disease prediction ensemble learning hypertension imbalanced data outlier data
Online Access:	https://ieeexplore.ieee.org/document/8854986/

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spelling	doaj-18424a7f60d0443e8e7680166cb54ee12021-03-30T00:13:03ZengIEEEIEEE Access2169-35362019-01-01714477714478910.1109/ACCESS.2019.29451298854986Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and HypertensionNorma Latif Fitriyani0https://orcid.org/0000-0002-1133-3965Muhammad Syafrudin1https://orcid.org/0000-0002-5640-4413Ganjar Alfian2Jongtae Rhee3Department of Industrial and Systems Engineering, Dongguk University, Seoul, South KoreaDepartment of Industrial and Systems Engineering, Dongguk University, Seoul, South KoreaNano Information Technology Academy, Dongguk University, Seoul, South KoreaDepartment of Industrial and Systems Engineering, Dongguk University, Seoul, South KoreaEarly diseases prediction plays an important role for improving healthcare quality and can help individuals avoid dangerous health situations before it is too late. This paper proposes a disease prediction model (DPM) to provide an early prediction for type 2 diabetes and hypertension based on individual's risk factors data. The proposed DPM consists of isolation forest (iForest) based outlier detection method to remove outlier data, synthetic minority oversampling technique tomek link (SMOTETomek) to balance data distribution, and ensemble approach to predict the diseases. Four datasets were utilized to build the model and extract the most significant risks factors. The results showed that the proposed DPM achieved highest accuracy when compared to other models and previous studies. We also developed a mobile application to provide the practical application of the proposed DPM. The developed mobile application gathers risk factor data and send it to a remote server, so that an individual's current condition can be diagnosed with the proposed DPM. The prediction result is then sent back to the mobile application; thus, immediate and appropriate action can be taken to reduce and prevent individual's risks once unexpected health situations occur (i.e., type 2 diabetes and/or hypertension) at early stages.https://ieeexplore.ieee.org/document/8854986/Diabetesdisease predictionensemble learninghypertensionimbalanced dataoutlier data
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Norma Latif Fitriyani Muhammad Syafrudin Ganjar Alfian Jongtae Rhee
spellingShingle	Norma Latif Fitriyani Muhammad Syafrudin Ganjar Alfian Jongtae Rhee Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension IEEE Access Diabetes disease prediction ensemble learning hypertension imbalanced data outlier data
author_facet	Norma Latif Fitriyani Muhammad Syafrudin Ganjar Alfian Jongtae Rhee
author_sort	Norma Latif Fitriyani
title	Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension
title_short	Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension
title_full	Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension
title_fullStr	Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension
title_full_unstemmed	Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension
title_sort	development of disease prediction model based on ensemble learning approach for diabetes and hypertension
publisher	IEEE
series	IEEE Access
issn	2169-3536
publishDate	2019-01-01
description	Early diseases prediction plays an important role for improving healthcare quality and can help individuals avoid dangerous health situations before it is too late. This paper proposes a disease prediction model (DPM) to provide an early prediction for type 2 diabetes and hypertension based on individual's risk factors data. The proposed DPM consists of isolation forest (iForest) based outlier detection method to remove outlier data, synthetic minority oversampling technique tomek link (SMOTETomek) to balance data distribution, and ensemble approach to predict the diseases. Four datasets were utilized to build the model and extract the most significant risks factors. The results showed that the proposed DPM achieved highest accuracy when compared to other models and previous studies. We also developed a mobile application to provide the practical application of the proposed DPM. The developed mobile application gathers risk factor data and send it to a remote server, so that an individual's current condition can be diagnosed with the proposed DPM. The prediction result is then sent back to the mobile application; thus, immediate and appropriate action can be taken to reduce and prevent individual's risks once unexpected health situations occur (i.e., type 2 diabetes and/or hypertension) at early stages.
topic	Diabetes disease prediction ensemble learning hypertension imbalanced data outlier data
url	https://ieeexplore.ieee.org/document/8854986/
work_keys_str_mv	AT normalatiffitriyani developmentofdiseasepredictionmodelbasedonensemblelearningapproachfordiabetesandhypertension AT muhammadsyafrudin developmentofdiseasepredictionmodelbasedonensemblelearningapproachfordiabetesandhypertension AT ganjaralfian developmentofdiseasepredictionmodelbasedonensemblelearningapproachfordiabetesandhypertension AT jongtaerhee developmentofdiseasepredictionmodelbasedonensemblelearningapproachfordiabetesandhypertension
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Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension

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