Heart Disease Diagnosis Using Fuzzy Supervised Learning Based on Dynamic Reduced Features

Walid Moudani, Mohamad Hussein, Mariam abdelRazzak and Félix Mora-Camino
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Walid Moudani: Doctorate School of Sciences and Technologies, Lebanese University, Lebanon
Mohamad Hussein: Doctorate School of Sciences and Technologies, Lebanese University, Lebanon
Mariam abdelRazzak: Doctorate School of Sciences and Technologies, Lebanese University, Lebanon
Félix Mora-Camino: MAIAA Laboratory, French Civil Aviation Institute-ENAC, France

International Journal of E-Health and Medical Communications (IJEHMC), 2014, vol. 5, issue 3, 78-101

Abstract: The health industry collects huge amounts of health data which, unfortunately, are not mined to discover hidden information. However, there is a lack of effective analytical tools to discover hidden relationships and trends in data. Information technologies can provide alternative approaches to the diagnosis of the heart attach disease. In this study, a proficient methodology for the extraction of significant patterns from the Coronary Heart Disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to develop an innovative fuzzy classification solution approach based on dynamic reduced sets of potential risk factors using the promising Rough Set theory which is a new mathematical approach to data analysis based on classification of objects. Therefore, we propose to validate the classification using Multi-classifier decision tree to identify the risky heart disease cases. This work is based on a dataset collected from several clinical institutions based on the medical profile of patient. Moreover, the experts' knowledge in this field has been taken into consideration in order to define the disease, its risk factors, to follow up the issue results, and to establish significant knowledge relationships between medical factors related to Coronary Heart Disease. To identify cases of heart attack, experiments of several classification techniques have been performed leading to rank the suitable techniques. The reduction of potential risk factors contributes to enumerate dynamically one or more optimal subsets of the potential risk factors of high interest which implicitly leads to reduce the complexity of the classification problems while maintaining the prediction classification quality. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Date: 2014
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