Early detection of Diabetes using Machine Learning Techniques

Abstract

Diabetes is understood to be an ailment where the human being system's blood sugar amounts tend to be unusually higher. It's acknowledged all over the globe among the long-term problems. Diabetes prevents your body's capability to help to make insulin, leading to extreme blood sugar levels as well as gluconeogenesis abnormalities. A lot of women are influenced by gestational diabetes, the industry type of diabetes occurring throughout being pregnant. Ladies tend to be more likely compared to guys to build up diabetes-related difficulties, as well as women that are pregnant may create gestational diabetes throughout their pregnancy. The recent advancement of Machine learning (ML) provides a significant part in illness detection and prediction upon many phenomena. This makes ML great techniques to predict diabetic disease prediction. This research chose the well-known Logistic Regression (LgR), k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Random Forests (RF), XGBoost, and LightGBM, for diabetes prediction. A comparative study of the algorithmic performances is performed to identify the best valuable algorithm in the clinical decisions system. In the experiment, the LightGBM classifier gives the highest accuracy (88.5%) for the diabetes detection. Furthermore, this article has also compared the proposed work with existing state-of-art works. Results found that the proposed model gives better results than existing work.