糖尿病
机器学习
朴素贝叶斯分类器
逻辑回归
算法
人工智能
计算机科学
精确性和召回率
医学
支持向量机
内分泌学
作者
Fayroza Alaa Khaleel,Abbas M. Al-Bakry
标识
DOI:10.1016/j.matpr.2021.07.196
摘要
One of the chronic diseases is Diabetes, Diabetes is a metabolic disorder category caused by continued high levels of blood sugar. It is regarded as one of the most deadly diseases in the world. If accurate early prediction is possible, Diabetes severity and risk factor can be significantly lowered. Machine learning has become more popular in the medical community as a result of its ascent, and in the field of diseases in particular. In this paper, we propose a model that can predict where the patient has or hasn't have diabetes. Our model is based on the prediction precision of certain powerful machine learning (ML) algorithms based on different measures such as precision, recall, and F1-measure. The Pima Indian Diabetes (PIDD) dataset has been used, that can predict diabetic onset based on diagnostics manner. The results we obtained using Logistic Regression (LR), Naïve Bayes (NB), and K-nearest Neighbor (KNN) algorithms were 94%, 79%, and 69% respectively. The results show that LR is more efficient at predicting diabetes Compared to other algorithms.
科研通智能强力驱动
Strongly Powered by AbleSci AI