An Optimized Approach for Prediction of Heart Diseases using Gradient Boosting Classifier

ABSTRACT Data mining in medical domain will yield in discovering and retreating valuable patterns and knowledge which might prove useful in clinical identification. Disease Prediction system supported modeling predicts the diseases. The system analyses the diseases provided by the record of patient as input and provides the results as an output. Disease Prediction is done by implementing the data mining techniques like support vector machine (SVM), Logistic regression and optimized gradient boosting classifier (GBC). During this work, uses fourteen medical attributes like age, sex, blood pressure, cholesterol, fasting blood sugar (FBS), thalach etc. for prediction. The dataset contains 284 records of patients. The various techniques used for predicting the risk level of every person supported age, gender, blood pressure, fasting blood sugar (FBS) cholesterol, pulse rate etc. This work examines totally different the various data mining algorithms and compares the results with cross validation or without cross validation and different performance measures, i.e. accuracy, precision, recall and f1-score. The accuracy of the model applying on the GBC technique with cross validation and while not cross validation is calculated. Then the one with an accuracy is taken which is in the form of prediction result. Keywords: Gradient boosting classifier (GBC), Logistic regression, Optimization techniques, Predictive Modeling.