Early Prediction of Diabetes Using Machine Learning

Abstract

This project focuses on leveraging machine learning techniques to develop a predictive model for the early detection of diabetes. With the growing prevalence of diabetes globally, timely diagnosis plays a crucial role in managing the condition and preventing its associated complications. The study utilises a dataset containing relevant health parameters such as glucose levels, BMI, age, and blood pressure, sourced from diverse demographic groups. Implemented within the Google Colab environment, various machine learning algorithms, including logistic regression, decision trees, and support vector machines, are employed to analyse the dataset and construct predictive models. Feature selection and engineering techniques are applied to enhance model performance and interpretability. Furthermore, the project explores the integration of deep learning methodologies for more nuanced pattern recognition. Model performance evaluation involves rigorous testing and validation. Additionally, the models are assessed for their ability to provide interpretable insights into the factors contributing to diabetes risk. The anticipated outcome of this research is the development of a robust and accurate predictive tool capable of identifying individuals at risk of diabetes at an early stage. Such a tool has the potential to facilitate proactive interventions and personalised healthcare strategies, ultimately contributing to improved patient outcomes and healthcare management efficiency.