Diabetes Prediction using Machine Learning

Tushar Kanti De, Prathipati Likhitha, J. Vamsi, T. K. Sai, S. Jaswanth, N. S. K. Teja, P. N. Raju
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Abstract

- Diabetes is a disease caused by a high level of glucose in the human body. Diabetes should not be ignored if it is not treated, then Diabetes can cause serious problems for a person such as: heart problems, kidney problems, high blood pressure, eye damage and can affect other parts of the human body. Curing diabetes can be easier if it is predicted earlier. In order to achieve this project goal we will be able to predict Diabetes in the human body or patient with the highest accuracy by applying, Various Machine Learning Strategies. Machine learning is used to predict accurate values with the available set of patient informations so the accurate values of the diabetes based on the individual can be predicted accurately. In this activity we will use the Learning Machine Planning and compile data to predict diabetes. Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Neighboring K-Nearest (KNN), Gradient Boosting (GB) and Random Forest (RF). Accuracy is different for all models compared to other models. Project Work gives a more exact or precise model appearance that the model can precisely foresee diabetes. Our outcome shows that Random Forest has accomplished higher exactness contrasted with other AI systems.
利用机器学习预测糖尿病
- 糖尿病是一种因人体内葡萄糖水平过高而引起的疾病。糖尿病不容忽视,如果不加以治疗,那么糖尿病会给人带来严重的问题,如:心脏问题、肾脏问题、高血压、眼睛损伤,还会影响人体的其他部位。如果能及早预测,治疗糖尿病就会变得更加容易。为了实现这个项目目标,我们将通过应用各种机器学习策略,以最高的准确率预测人体或患者的糖尿病。机器学习用于利用现有的患者信息集预测准确的数值,因此可以根据个人情况准确预测糖尿病的准确数值。在这项活动中,我们将使用学习机器规划和编译数据来预测糖尿病。逻辑回归(LR)、决策树(DT)、支持向量机(SVM)、最近邻 KNN(KNN)、梯度提升(GB)和随机森林(RF)。与其他模型相比,所有模型的准确度都有所不同。项目工作提供了一个更准确或更精确的模型外观,该模型可以精确地预测糖尿病。我们的结果表明,与其他人工智能系统相比,随机森林的精确度更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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