DESIGN AND IMPLEMENTATION OF AN AI-BASED MEDICAL ANALYTICS FRAMEWORK EMPLOYING DEEP NEURAL NETWORKS AND ADVANCED MACHINE LEARNING MODELS FOR PRECISION HEALTHCARE

Abstract

The digitalization of healthcare is fast, bringing about abundant and diverse medical data with novel opportunities to bring precise healthcare with the help of artificial intelligence (AI) associated analytics. Traditional methods of data analysis in medical fields frequently do not describe nonlinear and complex relationships in multimodal clinical data that can be utilised to specify diagnosis and treatment plans. In this review, the author has provided a critical examination of AI-based medical analytics for precise healthcare using deep neural networks and optimised machine learning models. Discuss the main medical data modalities, such as electronic health records, medical imaging, biomedical signals, omics data, and wearable sensor streams, and the implications they have on model selection. An organised taxonomy of deep learning networks, including convolutional, recurrent, transformer-based, and graph neural networks, is offered together with advanced machine learning solutions, including ensemble learning, probabilistic models, automated machine learning, and explainable AI. A data preprocessing end-to-end framework without involving model training, clinical decision support, and scalable deployment needs to be synthesised. The deep learning architectures achieved up to 98% accuracy in imaging tasks, but required integration with XAI for clinical validation. Lastly, there are issues of validation, interpretability, privacy, fairness, and compliance with regulations, which are addressed, and future directions in research to trustworthy and personalised AI-based health care systems are mentioned.

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