AI Quickens Diabetes Discovery: Remain Ahead with Innovation

Abstract

Machine learning presents a promising avenue towards the construction of accurate and effective diabetes prediction model. Diabetes is a worrying challenger to health, characterized by high levels of blood sugar. Its early diagnosis and interventions are very necessary for the detection and management of the disease and the avoidance of complications. Traditionally, the diagnosis of diabetes depends on clinical symptoms and blood tests. All these methods of diagnosis usually detect the disease in a very late stage complications already existed before diagnosis. The main objective of this study was to determine whether the learning ensemble technique can produce better accuracy and reliability of diabetes prediction, working by the integration of several algorithms of machine learning. Ensemble harnesses the learning strength of large numbers of machine algorithms to build a more powerful and accurate prediction engine. It reduces variance and improves accuracy, making it a solution to the overfitting problem, generally by using the diversity in the base models. In this paper light has been thrown that the ensemble learning performs better in most cases than the individual models because of the collective knowledge gained from training on the same dataset.