A Multifactorial Approach to Explain Risk Features for Predicting Survival Rate of Heart Failure

Heart failure is a chronic medical condition characterized by impaired contractile properties of the heart muscles, resulting in reduced blood supply within the body. According to the World Health Organization, heart failure is one of the most serious ailments that limit the patient’s activities and decreases their life span, contributing to the highest number of deaths globally. Highlighting the significance of risky factors related to the survival of patients with heart failure is crucial for effective intervention. In this chapter, we propose a new multifactorial approach to explain risk features for predicting the survival rate of heart failure using a multivariate logistic regression model. The experimental evaluation with a medical dataset collected from 299 patients diagnosed with heart failure shows that the most risk factors include age, ejection fraction rate, and serum creatinine level in terms of binary categories of patients with smoking, diabetes, high blood pressure, and anemia. In particular, the likelihood of patients experiencing a fatal event following heart failure will increase by 3.0 for each additional year of age. When the ejection fraction increases by 1%, the odds of patients experiencing a fatal event decrease by 2.66, and a one-unit rise in serum creatinine results in the odds of patients experiencing a fatal event following heart failure increasing by a factor of 0.41.