Unveiling Cardiac Insights: Evaluating Machine Learning Algorithms for Accurate Heart Disease Forecasting
Keywords:
Cardiovascular Disease, Clinical Prediction, Machine Learning Models, Predictive ModellingAbstract
Heart diseases, also referred to as cardiovascular diseases, include a diverse range of conditions that have an emotional control on the functioning of the heart. Heart failure is currently one of the world's top reasons of death. Developing a robust prediction system specific to this condition is imperative to address this challenge. Machine learning, a widely employed tool in data science, traditionally forecasts an output based on input data. In the current context, machine learning is applied to medical records to make clinical predictions regarding individual patients’ illnesses. The machine learning algorithms discern patterns within the provided input data and leverage this knowledge to predict the presence of diseases using real-world data. To indicate if patients have cardiac disease, this study investigates the use of several machine learning (ML) models, such as Logistic Regression, naive Bayes, decision tree models, Random forest models, XGBoost, Support Vector Machines (SVM), and Artificial Neural Networks (ANN). We perform extensive data preprocessing and exploratory data analysis (EDA) to ensure accurate and pertinent data. We use a dataset with 303 entries and 14 attributes, including age, sex, type of CP, resting BP serum cholesterol, blood sugar during fasting, the highest heart rate achieved, exercise-induced angina, old peak, slope, number of major blood vessels colored by fluoroscopy, and thalassemia. The outcomes specify that machine learning models, predominantly Random Forest, demonstrate significant potential in predicting heart disease and exhibit superior predictive performance of 95.08% accuracy.
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