Deep Learning-based Disease Predictive Model for Healthcare

Deep Learning-based Disease Predictive Model for Healthcare

Mohammad Anas1, Md Tabrez Nafis2, Nafisur Rahman3

1 Jamia Hamdard, New Delhi, Delhi-110019, India

2 Jamia Hamdard, New Delhi, Delhi-110019, India

3 Jamia Hamdard, New Delhi, Delhi-110019, India

Abstract. Recent advancements in healthcare underscore the critical need for innovative strategies to enhance patient outcomes. Traditional health information systems often falter when managing the immense and intricate volumes of data generated in medical settings. Deep Learning technologies provide an innovative approach, enabling the extraction of valuable insights from intricate datasets while requiring minimal human effort. Neural Health emerges as a trailblazing approach, seamlessly integrating deep learning into health information systems. Neural Health collects data from various sources, including electronic health records, medical imaging, genetic profiles and wearable devices, then processes and structures it for deep learning applications. Leveraging recurrent neural networks (RNNs), it delivers actionable insights across key healthcare domains, including medical diagnostics, treatment planning, predictive analytics, and personalized medicine. Preliminary research and clinical trials reveal that Neural Health significantly enhances healthcare outcomes. It excels in early disease direction, risk prediction, and improving patient satisfaction. Thanks to its scalable and adaptable deep learning architecture, Neural Health is poised to revolutionize health information technology, making it a versatile solution for diverse healthcare environments.

Keywords: Neural Health, Deep Learning, Electronic Health Records (EHRs), Healthcare Informatics, Medical Imaging.