用机器学习预测糖尿病，神经网络效果最佳。
A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes
- 对比8种算法在768例数据上预测糖尿病
- 神经网络准确率达78.57%，优于随机森林的76.30%
- 适合医疗辅助诊断与早期筛查研究者参考
在全球多个国家，糖尿病已成为重大健康问题，早期识别与控制至关重要。利用机器学习算法预测糖尿病已取得积极成果。本研究基于Pima Indians Diabetes数据集，评估多种机器学习方法在糖尿病预测中的有效性。该数据集包含768名患者的年龄、体重指数（BMI）、血糖水平等信息。评估的算法包括逻辑回归、决策树、随机森林、k近邻、朴素贝叶斯、支持向量机、梯度提升和神经网络。结果表明，神经网络表现最优，准确率为78.57%，其次为随机森林，准确率为76.30%。研究说明机器学习算法可有效辅助糖尿病预测，具备作为早期检测工具的潜力。
原文摘要 · Abstract (English)
In many nations, diabetes is becoming a significant health problem, and early identification and control are crucial. Using machine learning algorithms to predict diabetes has yielded encouraging results. Using the Pima Indians Diabetes dataset, this study attempts to evaluate the efficacy of several machine-learning methods for diabetes prediction. The collection includes information on 768 patients, such as their ages, BMIs, and glucose levels. The techniques assessed are Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting, and Neural Network. The findings indicate that the Neural Network algorithm performed the best, with an accuracy of 78.57 percent, followed by the Random Forest method, with an accuracy of 76.30 percent. The study implies that machine learning algorithms can aid diabetes prediction and be an efficient early detection tool.
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