Prediction of Diabetes Based on ML & DL and Visual Analysis

This article aims to conduct a comprehensive analysis and modeling of the diabetes prediction problem using machine learning and deep learning methods, along with visual analysis. First, we introduce the dataset used for prediction, detailing the source, content, and preprocessing of the data. Through exploratory data analysis (EDA), we examined missing values, duplicate values, the proportion of the target variable, data distribution, outliers, etc., to ensure data quality and prepare for modeling. Next, the article employs various machine learning algorithms, including Random Forest, K-Nearest Neighbors, Support Vector Machine, Decision Tree, AdaBoost, Logistic Regression, CatBoost Classifier, Naive Bayes, etc., to model the training set and evaluate the performance of each model. To enhance model performance, the article also uses ensemble learning methods, including Stacking, Soft Voting, and Hard Voting, to optimize the combination of different models, further improving prediction accuracy. Additionally, we trained a neural network based on deep learning, constructed a deep learning model using the Keras framework, and evaluated the model's effectiveness through loss function and accuracy curves. Finally, the article discusses possible future improvements, such as building a diabetes prediction website and implementing login and prediction pages, to further enhance the model's application value. Through these steps, we can accurately predict the occurrence of diabetes, with the trained model achieving an accuracy of 92%, effectively realizing the theme of AI for Science.