摘要
这项研究需要通过应用不同的机器学习方法来提高人体或患者早期糖尿病预测的准确性。通过使用患者数据集来产生更高精度的预测，接近创建机器学习模型。这项工作将在数据集上使用机器学习分类和集成方法，例如随机森林(RF)、梯度提升(GB)、决策树(DT)、k-最近邻(KNN)、逻辑回归(LR)和支持向量机(SVM)来预测糖尿病。与其他模型相比，每个模型的精度都有所下降。这项工作通过提供精确或更高的精度来证明模型的能力。本研究报告了不同机器学习算法的不同性能指标，如精确度、召回率、准确度、F1分数和敏感度。最后的实验结果表明，随机森林分类器优于其他方法。
Abstract
This research requires to improve the accuracy of early diabetic forecasting in a human body or patient by applying diverse machine learning approaches. Approaching to creation of machine learning models by using patient datasets to produce predictions with improved accuracy. This work will use machine learning classifcation and ensemble approaches, such as Random Forest (RF), Gradient Boosting (GB), Decision Tree (DT), K-nearest neighbour (KNN), Logistic Regression (LR), and Support Vector Machine (SVM), on a dataset to predict diabetes. The accuracy of each model difers in comparison to other models. This work demonstrates the model's capability by providing an accurate or greater accuracy. This research paper reported diferent performance metrics like precision, recall, accuracy, F1 score, and sensitivity for various machine learning algorithms. Final experi- mental results indicate that the Random Forest classifer outperforms other methods.
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