Optimizing Diabetes Classification with a Machine Learning-Based Framework

Background: Diabetes 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. Result: This paper proposes a machine learning-based diabetes classification framework MOG. The framework includes using the mean, median joint filling method to handle missing values, using the cap method for outlier processing, and then proposing a diabetes classification model based on the Generative Adversarial Network for Diabetes Classification (DCSGAN), and finally using logistic regression to analyze the features in detail. The model was tested using the PIMA dataset and the diabetes dataset in the GEO database, achieving an accuracy rate of 98.37% for binary classification and 96.75% for ternary classification in the PIMA dataset, and better performance than traditional models in the data from the GEO database. Conclusion: The experimental results show that the framework proposed in this paper can accurately classify diabetes and provide new ideas for intelligent diagnosis of diabetes.