用6种机器学习模型分析患者生活习惯与多种疾病关联性。
A Comparative Study on Machine Learning Models to Classify Diseases Based on Patient Behaviour and Habits
- 基于患者相关因素构建多疾病分类模型。
- 糖尿病、中风、心脏病与肾病均有显著预测效果。
- 提供网页工具实现心脏病风险实时预测，适合健康管理者使用。
近年来，机器学习算法在基于健康数据预测疾病方面展现出潜力，尤其在疾病建模领域。现有研究多采用监督学习方法，而医疗数据量每年急剧增长，涵盖身高、体重、年龄、血糖、血脂、胰岛素等患者相关因素（PRF），这些指标随时间持续变化。分析历史数据有助于识别疾病风险因素及其相互作用，提升诊断与预测准确性。本研究采用六种监督学习模型，系统探究PRF与糖尿病、中风、心脏病（HD）和肾病（KD）的关联；进一步分析糖尿病、中风、肾病与心脏疾病的交互关系。研究还比较并评估了不同算法在基于PRF分类多种疾病上的表现，包括心脏病、糖尿病、中风、哮喘、皮肤癌及肾病。最终通过一个基于Web的应用程序，以最准确的分类器为用户提供输入个人数据后的心脏病预测功能。
原文摘要 · Abstract (English)
In recent years, ML algorithms have been shown to be useful for predicting diseases based on health data and posed a potential application area for these algorithms such as modeling of diseases. The majority of these applications employ supervised rather than unsupervised ML algorithms. In addition, each year, the amount of data in medical science grows rapidly. Moreover, these data include clinical and Patient-Related Factors (PRF), such as height, weight, age, other physical characteristics, blood sugar, lipids, insulin, etc., all of which will change continually over time. Analysis of historical data can help identify disease risk factors and their interactions, which is useful for disease diagnosis and prediction. This wealth of valuable information in these data will help doctors diagnose accurately and people can become more aware of the risk factors and key indicators to act proactively. The purpose of this study is to use six supervised ML approaches to fill this gap by conducting a comprehensive experiment to investigate the correlation between PRF and Diabetes, Stroke, Heart Disease (HD), and Kidney Disease (KD). Moreover, it will investigate the link between Diabetes, Stroke, and KD and PRF with HD. Further, the research aims to compare and evaluate various ML algorithms for classifying diseases based on the PRF. Additionally, it aims to compare and evaluate ML algorithms for classifying HD based on PRF as well as Diabetes, Stroke, Asthma, Skin Cancer, and KD as attributes. Lastly, HD predictions will be provided through a Web-based application on the most accurate classifier, which allows the users to input their values and predict the output.
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