Diabetes Prediction Using Python Machine Learning Techniques

Diabetes is a disease that can originate in a person if their blood glucose levels are continually higher. Diabetes should not be neglected since, if left untreated, it can lead to complications with the heart, kidneys, blood pressure, and other systems in the body. Diabetes can be managed, however, if it is detected at an earlier stage. In order to achieve this goal, the work we are doing on this project will assist in the early prediction of diabetes in the body of a human being. The application of machine learning algorithms yields superior results when predicting whether or not a person will develop diabetes based on previous datasets compiled using the patient’s medical history. To predict diabetes, this model will employ Machine Learning Classifier and ensemble approaches on a dataset. In this model, we will test the accuracy of several different machine learning algorithms, evaluate the accuracy of each method, and then select the algorithm that provides the best fit while having the highest accuracy among the available algorithms. Many machine learning algorithms exist, such as the Support Vector Machine, the K-Nearest Neighbors (KNN), and the Random Forest Classifier (SVM).Nonetheless, we are utilizing the Random Forest Classifier since, compared to several other methods, it provides higher accuracy. This is because it is composed of several different decision trees. In addition, it makes use of randomization to improve its accuracy, prevent overfitting, and deliver a higher level of precision. We will use this Random Forest Classifier on the Pima dataset, and then, based on this earlier data, we will predict whether or not the individual has diabetes. The differentiation of system to other existing models, this project demonstrates a higher degree of accuracy. Also, we are integrating our model into the front end, which is simple to operate.