基于新数据集与机器学习，实现96.6%准确率的心脏病检测。
Multi-class heart disease Detection, Classification, and Prediction using Machine Learning Models
- 构建适用于孟加拉人群的新型心脏病数据集BIG-Dataset和CD-Dataset。
- 采用随机森林模型在测试集上达到96.6%的准确率。
- 适合医疗AI研究者与临床辅助诊断系统开发者参考。
心脏病是全球主要死因之一，尤其在中老年人群中更为突出，男性发病率更高。世界卫生组织（WHO）数据显示，非传染性疾病导致全球25%（1790万）死亡，其中孟加拉国每年有超过43,204例相关死亡。然而，针对孟加拉人群的心脏病检测（HDD）系统开发仍受限于缺乏基准数据集及依赖人工或有限数据的方法。本研究通过构建伦理合规的新数据集BIG-Dataset和CD-Dataset，整合症状、检查手段与风险因素信息，结合逻辑回归和随机森林等先进机器学习技术，在测试集上实现最高达96.6%的准确率。所提出的基于AI的系统可提供实时精准诊断与个性化健康建议，为可扩展、高效的心脏病检测提供创新方案，有望降低死亡率并改善临床结局。
原文摘要 · Abstract (English)
Heart disease is a leading cause of premature death worldwide, particularly among middle-aged and older adults, with men experiencing a higher prevalence. According to the World Health Organization (WHO), non-communicable diseases, including heart disease, account for 25\% (17.9 million) of global deaths, with over 43,204 annual fatalities in Bangladesh. However, the development of heart disease detection (HDD) systems tailored to the Bangladeshi population remains underexplored due to the lack of benchmark datasets and reliance on manual or limited-data approaches. This study addresses these challenges by introducing new, ethically sourced HDD dataset, BIG-Dataset and CD dataset which incorporates comprehensive data on symptoms, examination techniques, and risk factors. Using advanced machine learning techniques, including Logistic Regression and Random Forest, we achieved a remarkable testing accuracy of up to 96.6\% with Random Forest. The proposed AI-driven system integrates these models and datasets to provide real-time, accurate diagnostics and personalized healthcare recommendations. By leveraging structured datasets and state-of-the-art machine learning algorithms, this research offers an innovative solution for scalable and effective heart disease detection, with the potential to reduce mortality rates and improve clinical outcomes.
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