Diabetes Prediction Using Machine Learning Algorithm

Abstract

bstract: Diabetes, a chronic metabolic disorder affecting millions worldwide, requires early detection and management to mitigate its complications. Machine learning (ML) techniques have emerged as promising tools for predictive analytics in healthcare, offering the potential to improve diagnostic accuracy and patient outcomes. This paper presents a comprehensive review of ML algorithms applied to diabetes prediction, encompassing diverse methodologies and datasets. The study evaluates the performance of various ML algorithms, including but not limited to logistic regression, decision trees, support vector machines, random forests, and deep learning approaches, in predicting the onset or progression of diabetes. Additionally, feature selection techniques and data pre-processing methods are explored to enhance model robustness and interpretability. Furthermore, this review highlights the significance of dataset characteristics such as size, imbalance, and feature diversity in influencing model performance. Challenges associated with model interpretability, scalability, and deployment in clinical settings are also discussed, alongside potential strategies to address these issues. The findings suggest that ML algorithms demonstrate promising capabilities in diabetes prediction, with many studies reporting high accuracy, sensitivity, and specificity. However, there remains a need for standardized evaluation metrics and benchmark datasets to facilitate comparisons across studies. Moreover, efforts to enhance model interpretability and address data privacy concerns are crucial for promoting the adoption of ML-based predictive models in healthcare practice. In conclusion, this review underscores the potential of ML techniques in diabetes prediction and emphasizes the importance of interdisciplinary collaboration between data scientists, clinicians, and healthcare stakeholders to leverage these advancements for improved patient care and disease management