Enhanced decision support system to predict and prevent hypertension using computational intelligence techniques

Abstract

Medical decision support systems have been a core of intense research for years. The ongoing study shows that artificial intelligence has been accustomed to probe risk factors for hypertension. Factors, like health-damaging personal behaviors and changes in lifestyle and environment, are major contributors to chronic diseases. The goal of this research was to forecast the risk of developing hypertension by revealing hidden patterns in medical datasets. Quality of the data is the key to enhance the performance of learning model. But most healthcare data suffer from class imbalance problem, which induce the need for an intelligent model which can learn from such grimy data. This paper incorporates a novel approach by combing learning model and rule-based mining to offer decision support. Typically, the proposed work comprises two main implications. First suggests an intelligent learning model using boosting-based support vector machine to diagnose and expose multi-class categories in the imbalanced datasets. Finally, the enhanced predictive model is built upon the classification solution which will portray the innate data similarities. An intelligent fuzzy-based approach was employed to recognize frequent behavioral patterns. Based on these rules, valid decisions could be made to prevent hypertension. The suggested enhanced model is evaluated using a real-time hypertension dataset obtained through primary health centers. With the combination of ensemble strategies, the proposed intelligent learning model attains high classification accuracy for the imbalanced dataset above the traditional model. Thus, the efficient integration of personalized behavior with health data could provide a better understanding regarding patient health. In future this can serve as an eye toward personalized medicine.