Dibetes Prdiction Based on Body and Sysmptoms Parameters

Project Code :TCPGPY1831

Objective

The primary objective of this project is to develop a machine learning-based diabetes prediction system that integrates both clinical and symptom-based datasets for accurate risk assessment. The study aims to train and evaluate advanced machine learning models, including ANN, LSTM, Random Forest, and XGBoost, to identify patterns that indicate the likelihood of diabetes onset. The models will be assessed using key performance metrics such as accuracy, precision, recall, and F1-score to determine their effectiveness. Additionally, the project seeks to develop a web-based interface that allows healthcare professionals and individuals to input patient data and receive real-time diabetes risk predictions. Future objectives include optimizing model performance through hyperparameter tuning, incorporating additional datasets for improved generalizability, and integrating explainable AI techniques to enhance interpretability and trust in the prediction system.

Abstract

This study investigates the application of machine learning models—Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Random Forest, and XGBoost—for predicting the onset of diabetes using two distinct datasets. The first dataset contains clinical features such as age, glucose levels, BMI, and family history, while the second focuses on symptom-based indicators like polyuria, polydipsia, and weight loss. These datasets enable the models to identify patterns and relationships that may indicate an increased risk of diabetes. Model performance is evaluated using key metrics: accuracy, precision, recall, and F1-score. Among the models, XGBoost and Random Forest demonstrate strong predictive capabilities, effectively managing complex and non-linear feature interactions. ANN and LSTM also show promising results by capturing subtle patterns and temporal dependencies in the data. The system is designed to be deployed through a user-friendly web interface, making it accessible for both healthcare professionals and individuals for early diabetes risk assessment. This approach supports proactive healthcare decisions and timely intervention. Future enhancements include more advanced hyperparameter tuning and the integration of real-time data to boost model accuracy and reliability further. Ultimately, this system aims to contribute to improved diabetes prediction and early diagnosis using intelligent, data-driven solutions.

Keywords: Diabetes Prediction, Machine Learning, ANN, LSTM, Random Forest, XGBoost, Healthcare, Predictive Models, Early Diagnosis, Clinical Data, Symptom-Based Data, Web-Based Diagnosis System.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.