Disease diagnosis and prediction using deep learning: a review

Deep learning (DL) is a machine learning technique that processes data in a manner influenced by the functioning of the human brain. It is an effective tool for deciphering complicated data and may be applied to many other processes, such as decision-making, image recognition, and natural language processing. The requirement to process large amounts of data rapidly and precisely drives the demand for deep learning technologies in the healthcare industry. Deep learning can find patterns in medical data, including genomic data, patient records, and medical imaging. Additionally, it can be utilized to create prediction models that can aid clinicians in selecting the course of treatment for patients. This article employed deep learning models to examine medical data for better diagnoses. DL models efficiently improve accuracy, handle complicated medical data, and detect subtle trends. A comparative analysis of deep learning architectures revealed that DL helps boost diagnostic accuracy and recognize subtle disease patterns. However, issues like the need for vast training data, overfitting, model interpretability, and high computational resources exist. Also, we presented the applications in diagnosing heart disease, cancer, Alzheimer’s, and other specific diseases, demonstrating the potential of deep learning in predictive modeling for clinical decision support. This article comprehensively reviews deep learning architectures and comparative research for disease identification and prediction, and explores emerging solutions such as federated learning and explainable artificial intelligence (AI). The study also tackles research obstacles and potential advantages by presenting the current status and probable future directions of deep learning in disease diagnosis and prognosis.