Diabetes Prediction Using Machine Learning Algorithms and Hyperparameter Tuning for Expecting Mothers

Diabetes is a chronic disease that occurs when the blood glucose levels in the human body exceed the normal range. If left untreated, diabetes can lead to various health complications. To prevent such complications, early detection and management of diabetes are crucial. In this study, a novel system is proposed that uses hyperparameter tuning in addition to various machine learning algorithms for early detection of diabetes. This article also tunes the hyperparameters of the algorithms to optimize their performance. The results indicate that the Support Vector Machine algorithm provides the best fit for predicting diabetes. Hyperparameter tuning further improves the performance of this algorithm. Our approach can be used as a decision support tool for healthcare professionals to identify high-risk patients early on and provide appropriate care and management. Our study highlights the potential of machine learning algorithms and hyperparameter tuning for developing accurate and reliable predictive models for diabetes in expecting mothers. These models can aid healthcare professionals in taking preventive measures to improve maternal and child health outcomes.