ITDS | SPMS

DIABETES PREDICTIONUSING LSTM

Project Description
Supervisor

Diabetes mellitus is a chronic metabolic disorder affecting millions globally, leading to significant morbidity and mortality. Early prediction and diagnosis are critical for reducing complications and improving patient outcomes. Traditional diagnostic methods often miss early-stage risks. In this paper, we suggested a framework that applies the Long Short-Term Memory (LSTM) networks in order to forecast diabetes from the analysis of sequential and time-series data. The system applies the publicly available dataset of health indicators like glucose levels, BMI, insulin, blood pressure, and age after undergoing stringent preprocessing like selection of the features and normalization. The LSTM model is assessed using the measures like the accuracy, the precision, the recall, and AUC ROC. Outcomes reveal that the LSTM algorithm performs better compared to classical machine learning techniques, including Logistic Regression and Random Forest, due to the provision of the highest predictive precision and sensitivity. Furthermore, LSTM works well at capturing temporal dependencies that are not commonly detected using a traditional approach. The contribution enables high-level chronic disease management clinical decision-support systems that use the predictive healthcare capabilities of deep learning. Major Terms: Deep Learning, Diabetes Prediction, LSTM, Sequential Health Data, Machine Learning, Clinical Decision Support System, Forecasting of Chronic Diseases, Time-Series Modeling.

Machine Learning: Machine Learning (ML) research in Computer Science and Information Technology focuses on the development of algorithms and models that enable computers to learn from data and improve their performance over time without being explicitly programmed. It is a subset of Artificial Intelligence that uses statistical techniques to give machines the ability to learn patterns, make decisions, and predict outcomes based on data. Supervised learning, a key area of ML research, involves training models on labeled data, where the input-output relationships are predefined. This method is widely used for tasks such as classification (e.g., spam detection) and regression (e.g., predicting house prices). Unsupervised learning, on the other hand, involves finding hidden patterns in data without predefined labels, with clustering and association being typical applications in areas such as customer segmentation and anomaly detection. Reinforcement learning is another area of ML that focuses on teaching agents to make decisions by interacting with their environment and receiving feedback in the form of rewards or penalties. It is often applied in robotics, game playing, and autonomous systems, where continuous learning and adaptation are required.