Comparison of prediction models for coronary heart diseases in depression patients

Abstract

Globally, coronary heart diseases are one of the most common diseases and regarded as a cause of deaths. Prediction and management of such diseases with high mortality as well as occurrence rate (e.g., coronary heart diseases) are particularly critical. Often, coronary heart disease patients accompany depression symptoms hence, further accurate prediction and continuing management are warranted. Improper therapeutic treatments and failure of early detection of depression patients with coronary heart diseases may result serious clinical outcomes. Data mining, utilizing database, has been shown to aid for finding effective therapeutic patterns thereby pursuing qualitative improvement of medical treatments through diagnosis based on the dataset. In the current study therefore, we compared prediction models of coronary heart disease utilizing data-mining of depression patients data in order to develop the prediction model for coronary heart diseases of depression patients. In results, we demonstrated that the neural networks model predicted most accurately thus results herein may provide a basis of prediction model for coronary heart diseases in depression patients and be effective for the establishment of effective therapeutic treatments and management plans. © 2015 SERSC.