Authors:
Sunil Kumar Sehrawat
Addresses:
Department of Information Technology Management, Bausch Health Companies, Hillsborough, New Jersey, United States of America.
Abstract:
The application of artificial intelligence (AI) in the medical system has immense capability to redesign the diagnosis and treatment of chronic diseases during their initial stage. Early detection is required in an attempt to enhance patient outcomes, and utilization of patient information using AI algorithms can offer the pathway to identifying warning signs earlier. The study utilizes a big data set of the local hospital's electronic health record, such as patient characteristics, clinical histories, laboratory results, and life information. The information is analyzed through AI models, such as machine learning algorithms like decision trees, random forests, and neural networks, in order to predict the probability of the occurrence of disease. Analysis tools used are Python and frameworks like Scikit-learn for machine learning, TensorFlow for deep learning, and Matplotlib for plotting. AI models can diagnose diseases in the early stage before clinical signs and symptoms develop due to earlier patient data. The article also enlists the implementation of data fusion methodologies. The article attempts to convey significant developments in technology and improved strategies, as well as the day-to-day application and limitations of AI in identifying chronic diseases.
Keywords: Artificial Intelligence; Chronic Diseases; Early Detection; Patient Data Integration; Healthcare Technology; Cardiovascular Disease (CVD); Electronic Health Records (EHR); Diagnostic Machines.
Received: 29/01/2024, Revised: 29/03/2024, Accepted: 10/05/2024, Published: 05/09/2024
AVE Trends in Intelligent Health Letters, 2024 Vol. 1 No. 3 , Pages: 125-136