An Intelligent Model to Predict Cardiovascular Disease using Machine Learning Techniques

Abstract

Human life is the most important asset of human beings. Every year millions of people lose their lives to cardiovascular diseases. It is a group of diseases related to blood vessels and the heart. The chances of developing cardiovascular diseases in a person can be controlled by reducing some risk factors that cause them. If they are predicted timely in patients, the patients can take decisions and make changes to their lifestyles, and consequently reduce the risk of developing cardiovascular diseases. The proposed model gave very promising results. It has proven to be very efficient in predicting cardiovascular disease in a person using the Gradient Boosting Tree algorithm. The model had 78.78%, 76.78%, 81.10%, 82.43%, and 18.90% accuracy, sensitivity, specificity, miss rate, and precision, respectively. Moreover, the fallout, LR+, LR-, and NPV were 18.90%, 4.06, 3.82, and 75.16% respectively. The classification time was 13 milliseconds per record and the detection time was approximately 0.2137 seconds per record. The proposed model also outperformed various well-known machine learning algorithms and state-of-the-art models.

Keywords Heart, Adaptation models, Sensitivity, Machine learning algorithms, Medical services