Prediction of Diabetes Using Deep Learning Algorithms

Diabetes is a chronic disease posing significant health challenges worldwide, necessitating early and accurate diagnosis to prevent severe complications. This study explores the potential of deep learning algorithms, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, in predicting diabetes. Leveraging a comprehensive dataset of patient medical records, including demographic information, lifestyle factors, and biometric readings, the research aims to develop a robust predictive model. CNNs are employed to automatically extract relevant features from complex, multidimensional data, while LSTMs are utilized for their proficiency in handling time-series data, capturing temporal dependencies within patient records. The integration of these models aims to enhance the predictive accuracy, providing a nuanced analysis of both static and sequential data. The proposed hybrid model is trained and validated using a substantial dataset, demonstrating superior performance in predicting diabetes compared to traditional machine learning approaches. Results indicate that the combined CNN-LSTM model achieves a high degree of accuracy, sensitivity, and specificity, showcasing its potential as a valuable tool in the early diagnosis of diabetes. This research underscores the efficacy of deep learning techniques in healthcare applications, paving the way for more advanced, automated diagnostic systems.

Keywords: CNN and LSTM