MIFH: A Machine Intelligence Framework for Heart Disease Diagnosis

Cardiovascular disease tops the list among all major causes of deaths worldwide. Though, prognostication and in-time diagnosis can help in reducing the mortality rate as well as increases the survival rate of patients. Unavailability or scarcity of radiologists and doctors in different countries due to several reasons is a significant factor for hindrance in early diagnosis. Among various efforts of developing the decision support systems, computational intelligence is an emerging trend in the field of medical imaging to detect, prognosticate and diagnose the disease. It helps radiologists and doctors to get relief from being over-burdened and minimizes the induced delays for in-time diagnosis of patients. In this work, a machine intelligence framework for heart disease diagnosis MIFH has been proposed. MIFH utilizes the factor analysis of mixed data (FAMD) to extract as well as derive features from the UCI heart disease Cleveland dataset and train the machine learning predictive models. The framework MIFH is validated using the holdout validation scheme. Experimentation results show that MIFH performed well over several baseline methods of recent times in terms of accuracy and comparable in terms of sensitivity and specificity. MIFH returns best possible solution among all input predictive models considering performance criteria and improves the efficacy of the system, hence can assist doctors and radiologists in a better way to diagnose heart patients.