Breaking Boundaries: Harnessing the Power of Deep Learning Model Design to Predict Diabetes with Artificial Intelligence Assisted Prediction Strategy

Predicting the onset of diabetes at an early stage might greatly mitigate the disease’s devastating effects; the condition is currently incurable and results from chronically elevated blood sugar levels. Predicting the onset of diabetes has been the focus of current efforts within the deep learning community, building on decades of prior study. Nowadays, diabetes is a common and unfortunately incurable health problem. People can’t give their health the attention it needs because of their busy schedules. The food we eat is broken down into glucose, which is then released into our bloodstream. When blood sugar levels are too high, the pancreas secretes a hormone called insulin. A key function of insulin is to facilitate the uptake of glucose by cells for energy production. Identifying diabetes at an early stage can greatly improve the chances of leading a sustainable life. A fruitful way would be to use machine learning algorithms, which can improve themselves with future predictions after being taught and tested with massive amounts of data. This study presents AI-based Diabetes Prediction (AIDP), a model that explores diabetes data to make better predictions about the illness, with the severity of the condition in mind. The suggested rationale is tested against the traditional K-Nearest Neighbor (kNN) learning paradigm to see how well it works. Results from the planned study demonstrate that the model can accurately forecast the onset of diabetes, if not better. When compared to another deep learning method known as kNN, our results reveal that the suggested AIDP attained superior accuracy.