Machine Learning-Driven Diabetes Prediction: A

Serious health issues like heart disease, renal disease, stroke, blindness, and nerve damage are linked to diabetes mellitus.Its development is caused by a number of factors, including age, obesity, inactivity, heredity, lifestyle, bad nutrition, and hypertension.In medical settings, diagnosing diabetes usually entails a number of tests, with the findings guiding treatment decisions.In the healthcare industry, big data analytics is crucial because it makes it possible to analyze enormous datasets and find hidden patterns and insights that might improve forecast accuracy.However, the accuracy of many diabetes categorization and prediction algorithms now in use is limited.This study suggests a sophisticated diabetes prediction model that takes into account environmental variables that affect diabetes in addition to conventional measures like age, insulin, BMI, and glucose.By using a larger dataset, our model outperforms conventional methods in terms of classification accuracy.To further improve forecast accuracy, we also present a simplified process.Through the improvement of prediction models, this study demonstrates how Big Data Analytics may help with early diabetes detection and better patient outcomes.