Dialearner: Predicting Diabetes with Machine Learning

Abstract

Abstract: In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the mostdangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes. To predict the disease, it is extremely important to understandits symptoms. Currently, machine-learning (ML) algorithms are valuable for disease detection. Diabetes, in all its types, costs countries of all income levels unacceptably enormous personal, societal, and economic expenses.The proposed system can help doctors to make data-driven decisions and enhance patients’ treatment. Several machine learning algorithms that are Decision Tree, Support Vector Machine, Random Forest, Artificial NeuralNetwork, k-Nearest Neighbors, Logistic Regression, and Naive Bayes are used. Evaluation metrics such as accuracy, precision, recall, and F1-score are utilized to assess the model's predictive capability. Cross-validationtechniques are employed to ensure robustness and generalizability. The proposed model holds significant promisein facilitating early detection and intervention for individuals at risk of developing diabetes, thereby improving patient outcomes, and reducing healthcare burden. Future research directions may include incorporating additional features and exploring ensemble learning techniques to further enhance predictive accuracy and reliability