Optimizing diabetes classification with a machine learning-based framework

被引:5
作者
Feng, Xin [1 ,2 ,3 ]
Cai, Yihuai [1 ]
Xin, Ruihao [4 ,5 ,6 ]
机构
[1] Jilin Inst Chem Technol, Sch Sci, Jilin 130000, Peoples R China
[2] Jilin Univ, Coll Chem, State Key Lab Inorgan Synth & Preparat Chem, Changchun 130012, Peoples R China
[3] Jilin Univ, Sch Publ Hlth, Dept Epidemiol & Biostat, Changchun 130012, Peoples R China
[4] Jilin Inst Chem Technol, Coll Informat & Control Engn, Jilin 130000, Peoples R China
[5] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
[6] Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China
关键词
Diabetes diagnoses; Machine learning; GAN;
D O I
10.1186/s12859-023-05467-x
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
BackgroundDiabetes is a metabolic disorder usually caused by insufficient secretion of insulin from the pancreas or insensitivity of cells to insulin, resulting in long-term elevated blood sugar levels in patients. Patients usually present with frequent urination, thirst, and hunger. If left untreated, it can lead to various complications that can affect essential organs and even endanger life. Therefore, developing an intelligent diagnosis framework for diabetes is necessary.ResultThis paper proposes a machine learning-based diabetes classification framework machine learning optimized GAN. The framework encompasses several methodological approaches to address the diverse challenges encountered during the analysis. These approaches encompass the implementation of the mean and median joint filling method for handling missing values, the application of the cap method for outlier processing, and the utilization of SMOTEENN to mitigate sample imbalance. Additionally, the framework incorporates the employment of the proposed Diabetes Classification Model based on Generative Adversarial Network and employs logistic regression for detailed feature analysis. The effectiveness of the framework is evaluated using both the PIMA dataset and the diabetes dataset obtained from the GEO database. The experimental findings showcase our model achieved exceptional results, including a binary classification accuracy of 96.27%, tertiary classification accuracy of 99.31%, precision and f1 score of 0.9698, recall of 0.9698, and an AUC of 0.9702.ConclusionThe experimental results show that the framework proposed in this paper can accurately classify diabetes and provide new ideas for intelligent diagnosis of diabetes.
引用
收藏
页数:20
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