RFFE-Random Forest Fuzzy Entropy for the classification of Diabetes Mellitus

被引:0
|
作者
Ruby, A. Usha [1 ,2 ]
Chandran, J. George Chellin [1 ,2 ]
Jain, T. J. Swasthika [3 ]
Chaithanya, B. N. [3 ]
Patil, Renuka [3 ]
机构
[1] VIT Bhopal Univ, Sch Comp Sci, Bhopal Indore Highway, Sehore 466114, Madhya Pradesh, India
[2] VIT Bhopal Univ, Engn Dept, Bhopal Indore Highway, Sehore 466114, Madhya Pradesh, India
[3] GITAM Sch Technol, Dept Comp Sci & Engn, Doddaballapura 561203, Karnataka, India
来源
AIMS PUBLIC HEALTH | 2023年 / 10卷 / 02期
关键词
diabetes diseases; Fuzzy Entropy; machine learning; Synthetic Gradient Descent Technique; PREDICTION; DIAGNOSIS; COLOR; RISK;
D O I
10.3934/publichealth.2023030
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Diabetes is a category of metabolic disease commonly known as a chronic illness. It causes the body to generate less insulin and raises blood sugar levels, leading to various issues and disrupting the functioning of organs, including the retinal, kidney and nerves. To prevent this, people with chronic illnesses require lifetime access to treatment. As a result, early diabetes detection is essential and might save many lives. Diagnosis of people at high risk of developing diabetes is utilized for preventing the disease in various aspects. This article presents a chronic illness prediction prototype based on a person's risk feature data to provide an early prediction for diabetes with Fuzzy Entropy random vectors that regulate the development of each tree in the Random Forest. The proposed prototype consists of data imputation, data sampling, feature selection, and various techniques to predict the disease, such as Fuzzy Entropy, Synthetic Minority Oversampling Technique (SMOTE), Convolutional Neural Network (CNN) with Stochastic Gradient Descent with Momentum (SGDM), Support Vector Machines (SVM), Classification and Regression Tree (CART), K-Nearest Neighbor (KNN), and Naive Bayes (NB). This study uses the existing Pima Indian Diabetes (PID) dataset for diabetic disease prediction. The predictions' true/false positive/negative rate is investigated using the confusion matrix and the receiver operating characteristic area under the curve (ROCAUC). Findings on a PID dataset are compared with machine learning algorithms revealing that the proposed Random Forest Fuzzy Entropy (RFFE) is a valuable approach for diabetes prediction, with an accuracy of 98 percent.
引用
收藏
页码:422 / 442
页数:21
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