Prediction of Diabetes using Machine Learning Algorithms

In this work, we utilize machine learning to build a diabetes risk prediction model which is uniquely suited to a dataset of only females. The dataset focuses on important health measurements of women, such as the frequency of pregnancies and blood tests for insulin and glucose. We develop a Support Vector Machine (SVM) algorithm using NumPy, Pandas, and other Python packages. SVM is a good fit for the complex interplay of factors in diabetes risk prediction because of its ability to handle high-dimensional datasets and non-linear interactions. The approach uses historical data to train our SVM model, which enables the computer identify the patterns and correlations inside the dataset. Through the identification of minor associations between physiological indicators and pregnancy history, the model gains proficiency in forecasting the probability of developing diabetes.