ABSTRACT
Heart disease has turned into the most prevailing disease since the decades. But considering a few years back, it has played a vital role in taking human's life's irrespective of their lifestyle and food habits. The main aim of presenting this research is to develop an intelligent healthcare framework with the different machine learning techniques for the earliest prediction of the heart disease in the patient. The dataset is taken from kaggle. In the proposed methodology, we have used computational intelligence techniques such as logistic regression, K-NN, SVM and so on and obtained the highest accuracy of 99.75% in Decision Tree Classifier. Then an Ensemble model was proposed by considering several different classifiers and by hypertuning the parameters we could get more accurate prediction for the patient's heart disease. Then the incorporation of XAI techniques such as LIME and SHAP, provided a nuanced understanding of feature importance and the individual predictions. The amalgamation of the machine learning classifier, hyperparameter tuning, and the Explainable AI techniques which helped in interpreting the predictions made by machine learning model contributed in elevating the model interpretability.
