用机器学习预测慢性病，提升早期诊断准确率。
Chronic Diseases Prediction using Machine Learning and Deep Learning Methods
- 对比多种算法，用集成学习方法提升预测性能。
- 随机森林和梯度提升树在准确率与召回率上表现最优。
- 适合医疗数据科学家和健康系统优化研究者参考。
心血管疾病、糖尿病、慢性肾病和甲状腺疾病等慢性病是全球早亡的主要原因。早期检测与干预对改善患者预后至关重要，但传统诊断方法常因病情复杂而失效。本研究探讨了机器学习（ML）与深度学习（DL）在预测慢性病及甲状腺疾病中的应用。采用逻辑回归（LR）、随机森林（RF）、梯度提升树（GBT）、神经网络（NN）、决策树（DT）和朴素贝叶斯（NB）等多种模型，对数据进行完整预处理（包括缺失值处理、类别编码与特征聚合），并基于精确率、召回率、准确率、F1分数和受试者工作特征曲线下面积（AUC）评估模型性能。结果表明，随机森林与梯度提升树等集成方法表现更优；神经网络在捕捉复杂数据模式方面也展现出显著优势。研究证实，ML与DL有望推动慢性病预测的变革，实现早期诊断与个性化治疗。然而，仍面临数据质量、模型可解释性及医疗领域算力需求等挑战。本研究为大数据课程项目，受阿卜德拉赫曼·埃扎胡特与阿卜杜萨马德·埃萨伊迪教授指导。
原文摘要 · Abstract (English)
Chronic diseases, such as cardiovascular disease, diabetes, chronic kidney disease, and thyroid disorders, are the leading causes of premature mortality worldwide. Early detection and intervention are crucial for improving patient outcomes, yet traditional diagnostic methods often fail due to the complex nature of these conditions. This study explores the application of machine learning (ML) and deep learning (DL) techniques to predict chronic disease and thyroid disorders. We used a variety of models, including Logistic Regression (LR), Random Forest (RF), Gradient Boosted Trees (GBT), Neural Networks (NN), Decision Trees (DT) and Native Bayes (NB), to analyze and predict disease outcomes. Our methodology involved comprehensive data pre-processing, including handling missing values, categorical encoding, and feature aggregation, followed by model training and evaluation. Performance metrics such ad precision, recall, accuracy, F1-score, and Area Under the Curve (AUC) were used to assess the effectiveness of each model. The results demonstrated that ensemble methods like Random Forest and Gradient Boosted Trees consistently outperformed. Neutral Networks also showed superior performance, particularly in capturing complex data patterns. The findings highlight the potential of ML and DL in revolutionizing chronic disease prediction, enabling early diagnosis and personalized treatment strategies. However, challenges such as data quality, model interpretability, and the need for advanced computational techniques in healthcare to improve patient outcomes and reduce the burden of chronic diseases. This study was conducted as part of Big Data class project under the supervision of our professors Mr. Abderrahmane EZ-ZAHOUT and Mr. Abdessamad ESSAIDI.
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