用机器学习预测心脏病，随机森林准确率达91%
A Comprehensive Machine Learning Framework for Heart Disease Prediction: Performance Evaluation and Future Perspectives
- 基于303个样本构建多模型框架，通过调参提升预测性能
- 随机森林准确率91%，F1-score达0.89，分类表现均衡
- 适用于临床辅助诊断，适合医疗AI研究者参考
本研究提出一种基于机器学习的心脏病预测框架，使用包含303个样本、14个特征的心脏病数据集。方法包括数据预处理、模型训练与评估，采用逻辑回归、K近邻（KNN）和随机森林三类分类器，并通过GridSearchCV与RandomizedSearchCV进行超参数调优。随机森林表现最佳，准确率达91%，F1-score为0.89。精度、召回率及混淆矩阵分析显示各类别表现均衡。该模型在辅助临床决策方面展现出良好潜力。局限性在于数据集规模较小且多样性不足，未来研究需采用更大更广的数据集。本工作凸显了机器学习在医疗领域的应用价值，为预测诊断技术发展提供启示。
原文摘要 · Abstract (English)
This study presents a machine learning-based framework for heart disease prediction using the heart-disease dataset, comprising 303 samples with 14 features. The methodology involves data preprocessing, model training, and evaluation using three classifiers: Logistic Regression, K-Nearest Neighbors (KNN), and Random Forest. Hyperparameter tuning with GridSearchCV and RandomizedSearchCV was employed to enhance model performance. The Random Forest classifier outperformed other models, achieving an accuracy of 91% and an F1-score of 0.89. Evaluation metrics, including precision, recall, and confusion matrix, revealed balanced performance across classes. The proposed model demonstrates strong potential for aiding clinical decision-making by effectively predicting heart disease. Limitations such as dataset size and generalizability underscore the need for future studies using larger and more diverse datasets. This work highlights the utility of machine learning in healthcare, offering insights for further advancements in predictive diagnostics.
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