The health care industries collect huge amounts of data that contain some hidden information, which is useful for making effective decisions. For providing appropriate results and making effective decisions on data, some advanced data mining techniques are used. Among various life-threatening diseases, heart disease has garnered a great deal of attention in medical research. The diagnosis of heart disease is a challenging task, which can offer automated prediction about the heart condition of patient so that further treatment can be made effective. The diagnosis of heart disease is usually based on signs, symptoms and physical examination of the patient. There are several factors that increase the risk of heart disease, such as smoking habit, body cholesterol level, family history of heart disease, obesity, high blood pressure, and lack of physical exercise. Data Mining Techniques help in predicting the Heart diseases, and the predictions made are quite accurate.
The project involves analysis of the heart disease patient dataset with data processing. Then, different models were trained and predictions are made with the algorithms like Decision Tree,Naive Bayes,KNN.The Dataset we've used for our project is from Kaggle kernel 'Heart Disease Prediction Predicting probability of heart disease in patients.'
Heart Disease Prediction is a classification problem, with input features as a variety of parameters like Age,Sex,Chest pain type,BP,Cholesterol,FBS Over 120,ECG Results,Max HR,Exercise Angima,ST Depression and the target variable as a binary variable, predicting whether heart disease is present or not. We've achieved an accuracy of 85.19% using Decision Tree Technique.
Dataset used:https://www.kaggle.com/rishidamarla/heart-disease-prediction