Predictive Model on Cardiovascular-Disease using Ensemble Techniques - Supervised Classification Algorithms in Health Care Industries

Abstract

Background: Cardiovascular Diseases are rapidly growing in all kinds of aged people beyond the gender specification because of unhealthy diet, stress, lack of regular exercise, busy work schedule, weight, alcoholic and some other factors which are creating cardiovascular diseasesproblems. It is typical chronic illnesses with a high recurrence rate in health-related industries. In some of the cases, a heart attack occurs suddenly without any omens. Patients typically live in their homes rather than in hospitals and are often unable to access medical care in an emergency. Cardiovascular disease leads to a significant difficulty for the doctors to know the patient‟s status in time, and it becomes one of the significant reasons for death. Method: The researcher proposed a predicting model using ensemble techniques with different machine learning algorithms and to optimizethe accuracy of predictive model on cardiovascular-diseases problem. It is used and explored the accuracy of decision tree classifiers, random forest, K-neighborhood and support vector machine classifiers to find out which predictive model is more efficient for the accuracy point of view to predict cardiovascular-diseases problem in patients based on their health previous history. The proposed predictive model is more accurate and approved that support vector machine (SVM) gives good result than another predictive model. So the researcher accepted and adopted support vector machine SVM classier to predict
whether a person has cardio or not with good accuracy of 73%. Results: The accuracy of predictive model shows that Decision Tree Classifier 63%, Random Forest Classifier 70%, K-Neighbors Classifier 72%, and finally Support Vector Machine (SVM) classifier produced the 73%. As per the data analysis of accuracy level of algorithms, we can see that the SVM and KNN are performing better than other models. The researcher found thatSVM gives a better result than other models,in terms of accuracy
score, Auc score and F1_score, and SVMgives good result. So the researcher decided to accept and adopt support vector machine (SVM) classifier to predict whether a person has cardio or not with good accuracy of 73%. Conclusion:Finally the researcher concluded that the patient‟s age, weight,stress, cholesterol, smoking habits, alcoholic behaviors, irregular exercise,and unbalanced diet are the significant factors for a cardiovasculardisease problem in the real world. The proposed predictive model is more accurate and approved at Auc score and F1_score SVM gives good result. So the researcher assured that support vector machine (SVM) classifier to predict whether a person/patient has cardio or not with good accuracy of 73%.