Transforming Healthcare with Deep Learning Cardiovascular Disease Prediction

• Published in: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings
• Main Authors: Sasikala, V, Arunarasi, J, Surya, S, Shivaanivarsha, N, Guru, Raghavendra S, Gnanasudharsan, A
• Format: Conference Proceeding
• Language: English
• Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.01.2023
• Subjects: Ambient intelligence; Artificial intelligence; Cardiovascular diseases; Deep learning; Health care; Industrial electronics; Machine learning; Medical research
• DOI: 10.1109/AIKIIE60097.2023.10390290

Conference Title: 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE)Conference Start Date: 2023, Nov. 2 Conference End Date: 2023, Nov. 3 Conference Location: Ballari, IndiaDeep learning's introduction to the medical research community is changing the face of Artificial intelligence (AI) in healthcare. Recent advances in Deep Learning have made it possible to process and analyze massive volumes of medical data, such as photographs, genetic information, and clinical records. There will be far-reaching consequences for healthcare in the future due to this shift. The novelty of this research is developing unique, robust, effective, and economical to provide a transparent answer that will let the patient know, on a statistical level, whether or not a cardiac condition is likely to arise with the help of incorporating the bidirectional LSTM model (BDLSTM)with Catboost. Machine learning techniques can save treatment periods and money for patients and medical providers by eliminating the need for prolonged, costly clinical and laboratory investigations that are often unnecessary. The proposed Catboost model has a 92% success rate in diagnosing cardiac disease, an 88% success rate in diagnosing healthy individuals, and a precision of 93%.