Survival Rate Prediction Model of Cardio Vascular Disease Patients by Quantifying the Risk Profile using SVM

This research paper provides a clinical analysis of heart patient data to predict the survival rate of patients suffering from cardio-vascular diseases (CVD). The proposed solution will facilitate medical specialists in terms of providing quality health services to patients including the intensive treatments. Our model determines the chances of survival of patients suffering from any cardiovascular disease by analyzing the risks associated with them and other factors of their life style, physical activity, smoking habit, etc. The proposed survival rate prediction model is efficient and economical solution to facilitate the medical specialists in terms of following the most appropriate medical procedures for given symptoms. This paper presents an improved Stochastic Gradient Descent (iSGD) approach along with Hinge Loss Function of Support Vector Machine (SVM). Experimental results illustrate the effectiveness of the proposed prediction model in terms of predicting the survival rate of CVD patients.