A KNN Algorithm Based Predictive Model for Heart Disease Progression

Background: Disease diagnosis is a useful phenomenon in healthcare. Machine learning classification methods would considerably improve the healthcare industry by providing a quick diagnosis of the disease. Thus, time could be saved for doctors. Nearly 17.9 million people expire due to heart disease every year. Objectives: World Health Organization (WHO) predicted that rate of death might increase by 24.5 million in 2030. Since heart illness was the major cause of death in comparison with other diseases today, it was the most challenging disease to diagnose. Methods: One of the reasons for death due to heart disease was due to the fact that risks were not identified in the earlier stage. Earlier diagnosis of disease was very much important. Machine Learning algorithms were used for predicting the prognosis of disease. Results: Here K-NN algorithm was used to predict the presence of heart disease in an individual. Thus, patients were classified as either positive or negative for heart disease and this model enhanced medical care and reduced the cost. This gave us significant knowledge that helps us to predict the patients with heart disease. Conclusion: The Python sci-kit library was used to implement this in Anaconda Navigator's Spyder Integrated Development Environment. Experiments revealed that technique worked well and was more accurate than before.