Improving Heart Disease Prediction Accuracy Using a Hybrid Machine Learning Approach: A Comparative study of SVM and KNN Algorithms

The largest cause of mortality worldwide is heart disease, and early identification is critical in limiting disease development. Early approaches for detecting cardiovascular illnesses assisted in determining the progressions that should have happened in high-risk persons, reducing their risks. The major goal is to save lives by recognising anomalies in cardiac circumstances, which will be performed by identifying and analysing raw data produced from cardiac information. Machine learning can provide an efficient method for making decisions and creating accurate forecasts. Machine learning techniques are being used extensively in the medical business. A unique machine learning technique is provided in the proposed study to predict cardiac disease. The planned study made advantage of open source heart disease dataset from kaggle. Hybrid algorithms for machine learning prediction are the logical mixture of many previous methodologies designed to improve efficiency and produce improved outcomes. The presented work introduces a hybrid method that employs the notion of categorization for prediction analysis. We used real patient data to build a hybrid technique to predicting cardiac disease. KNN and SVM classification techniques were utilized in this paper. Jupyter Notebook is used to implement this hybrid method. A hybrid technique outperforms other algorithms in the prediction analysis of heart disease.