A Comprehensive Study on Deep Learning Techniques for Enhancing Predictive Performance in Healthcare Diagnostics

Abstract

The use of deep learning to implement diagnostics in the field of healthcare became one of the most critical fields of research, and may be used in predictive care with groundbreaking prospects. The given research is associated with a question on making more effective the mechanisms of the human conditions diagnosing in terms of introducing deep learning algorithms. We would like to mention, as a continuation of the existing approaches, the combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to analyse patient data and medical images. We seize the publicly available medical data so as to quantify the efficiency of various deep learning models, and we are focused on such metrics as accuracy, precision and recall. The results can suggest that there have been tremendously significant changes in the predictive behaves in cases where the CNN has been applied to radiology images, especially in identification of cancerous and non-cancerous tissues. The following outcome of our work highlights the necessity to introduce deep learning to the field of healthcare diagnostics, so among the possible avenues of the research, one may examine employing hybrid models and utilizing transfer learning strategies to increase precision. The given paper is the addition to the literature already overflowing with the articles on the possibilities of AI and healthcare and makes the knowledge about how these models may be used to assist in the early diagnosis and pre-treatment plans.