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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 9, 2018.
Abstract: This study aimed to develop a model for predicting the cardio-cerebrovascular disease of the South Korean elderly using the random forests technique. This study analyzed 2,111 respondents (879 males and 1,232 females), who were age 60 or older, out of total 7,761 respondents, who completed the Seoul Welfare Panel Study. The result variable was defined as the cardio-cerebrovascular disease (e.g., hypertension, cerebral infarction, hyperlipidemia, cardiac infarction, and angina). As a result of developing a random forest-based model, the major determinants of the cardio-cerebrovascular diseases of the South Korean elderly were mean monthly household income, the highest level of education, subjective health condition, subjective friendship, subjective family relationship, smoking, regular exercise, age, marital status, gender, depression experience, economic activity, and high-risk drinking. Among them, mean monthly household income was the most important predictor of the cardio-cerebrovascular disease. Based on the developed prediction model, it is needed to develop a systematic program for preventing the cardio-cerebrovascular disease of the Korean elderly.
Haewon Byeon, “Developing A Model to Predict the Occurrence of the Cardio-Cerebrovascular Disease for the Korean Elderly using the Random Forests Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 9(9), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090962
@article{Byeon2018,
title = {Developing A Model to Predict the Occurrence of the Cardio-Cerebrovascular Disease for the Korean Elderly using the Random Forests Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090962},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090962},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {9},
author = {Haewon Byeon}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.