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Analysis Study Research Paper | Computer Science and Information Technology | India | Volume 12 Issue 12, December 2023 | Rating: 4 / 10
Machine Learning Techniques for Precise Heart Disease Prediction
Abstract: Diagnosing and forecasting cardiovascular disease represents a pivotal task in medicine, crucial for accurately categorizing and effectively treating patients under the care of cardiologists. Within the medical domain, the integration of machine learning has grown, offering the capability to identify patterns from extensive datasets. Employing machine learning for the classification of cardiovascular disease occurrences holds promise in reducing diagnostic errors. This study introduces a novel method using k-modes clustering with Huang initialization to enhance the precision of classification. Various models, including random forest (RF), decision tree (DT), multilayer perceptron (MLP), and XGBoost (XGB), were employed and their parameters optimized using GridSearchCV. Evaluation was conducted on a practical dataset comprising 70,000 instances sourced from Kaggle, yielding the following accuracies: decision tree: 86.37% (with cross-validation) and 86.53% (without), XGBoost: 86.87% (with) and 87.02% (without), random forest: 87.05% (with) and 86.92% (without), multilayer perceptron: 87.28% (with) and 86.94% (without). Additionally, these models demonstrated robust AUC values: decision tree: 0.94, XGBoost: 0.95, random forest: 0.95, multilayer perceptron: 0.95. The study concludes that the multilayer perceptron model, particularly with cross-validation, exhibited superior performance with the highest accuracy of 87.28%.
Keywords: heart disease, machine learning, k-modes, classification, multilayer perceptron, model evaluation
Edition: Volume 12 Issue 12, December 2023,
Pages: 2120 - 2129