Automated Disease Diagnosis using Deep Learning

Abstract

In this study, a large dataset of medical records, including patient demographics, clinical measurements, and laboratory results, is employed to develop a robust deep learning model. The model utilizes state-of-the-art convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract valuable features from multi-modal data sources. These data sources encompass medical images (such as retinal scans and ultrasounds), textual information (patient history, symptoms, and lab reports), and genetic markers. The proposed deep learning model employs both supervised and unsupervised learning techniques. In the supervised phase, the model is trained on labeled data to predict diabetes status accurately. The unsupervised phase leverages the power of deep autoencoders and generative adversarial networks (GANs) to discover latent representations of data, aiding in feature extraction and anomaly detection. The evaluation of the model is conducted on a separate dataset, and its performance is compared to existing diagnostic methods, including traditional clinical assessments and machine learning approaches. The results demonstrate superior accuracy, sensitivity, and specificity in diabetes diagnosis, showcasing the potential of deep learning for improving healthcare outcomes.