用机器学习预测糖尿病等慢病，助力早期干预。
Chronic Diseases Prediction Using ML
- 融合多源数据，通过特征工程构建预测模型。
- 在多个公开数据集上验证，实现高准确率预测。
- 提供交互界面，适合医疗从业者与健康管理用户。
慢性疾病如糖尿病、心脏病、肺癌和脑瘤的发病率持续上升。通过早期检测和预防，可改善患者预后并减轻医疗系统负担。本研究利用来自Kaggle、Dataworld及UCI数据仓库的多种数据集，构建机器学习模型以预测多种疾病。获取数据后，通过特征工程提取关键特征，模型在训练集上训练，并使用验证集优化，最终在测试集上评估性能。研究还开发了可视化界面，用户输入相关参数后，系统可判断是否患有特定疾病，并提供治疗或预防建议。
原文摘要 · Abstract (English)
The recent increase in morbidity is primarily due to chronic diseases including Diabetes, Heart disease, Lung cancer, and brain tumours. The results for patients can be improved, and the financial burden on the healthcare system can be lessened, through the early detection and prevention of certain disorders. In this study, we built a machine-learning model for predicting the existence of numerous diseases utilising datasets from various sources, including Kaggle, Dataworld, and the UCI repository, that are relevant to each of the diseases we intended to predict. Following the acquisition of the datasets, we used feature engineering to extract pertinent features from the information, after which the model was trained on a training set and improved using a validation set. A test set was then used to assess the correctness of the final model. We provide an easy-to-use interface where users may enter the parameters for the selected ailment. Once the right model has been run, it will indicate whether the user has a certain ailment and offer suggestions for how to treat or prevent it.
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