Deep Learning Models for Genomic Health Monitoring in Intelligent Healthcare Systems

Abstract

Genomic health tracking is an important part of modern healthcare as it allows seeing genetic inclinations and offers individualized medicine. Conventional genomic analysis technologies have weaknesses in handling complex, high-dimensional genomic data, and hence, real-time monitoring of health is difficult. In this paper, the author will discuss how deep learning models, namely Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders, can be applied to improve genomic health monitoring. These models can autonomously discover intricate patterns in extensive genomic information to enhance the precision of disease forecasting, early diagnosis, and tailored treatment plans. The paper assesses the performance of these deep learning models based on the performance metrics of accuracy, sensitivity, specificity, F1-score, and AUC-ROC. The findings show that deep learning models are much more successful in comparison to classical algorithms, such as logistic regression and support vector machines (SVMs), in terms of accuracy and sensitivity. CNNs provided the best accuracy of 92%, whereas RNNs performed well with an AUC-ROC of 0.94, which demonstrated their ability to identify long-range correlations in genomic sequences. Autoencoders, which were applied to detect anomalies, detected rare mutations with a high precision rate (85 %). The deep learning process in genomic health monitoring is associated with considerable gains in both diagnostic accuracy and efficiency, and more automated and real-time healthcare systems. The paper will explain how these results can be used in intelligent healthcare systems and also suggest a direction of future work, especially in increasing model explainability and scalability in clinical practice. The practical implementation of deep learning models into healthcare systems should be the subject of further work aimed at enhancing patient outcomes