AN INTELLIGENT DATA ANALYTICS SYSTEM FOR THERAPEUTIC OF ANGINA PECTORIS IN CARDIOVASCULAR DISEASE MANAGEMENT: A MODEL METHODOLOGYAbstract
Medical Case taking process in any field of medicine science is considered to be a significant stage for the treatment management of various diseases. Cardio Vascular diseases (CVD) are one of the life-threatening diseases across the globe, and its prognosis entirely depends on the right intuition of the clinicians; cases may account for 31% of deaths worldwide, based on a recent study. Big Data Management and Machine Learning Algorithms have reached paramount Computational Technological advancement over the last decade and capable of drive applications in diverse domains which can assist in the process of selecting proper medicines. The case-taking approach and selection of drugs were significantly correlated, and the skill of the physicians plays a very important role in the entire procedure blended with proper use of Smart Technology, Artificial Intelligence, and Cloud storage of huge volume of patient’s physiological, clinical and pathological data. In this paper, we have made an attempt to build a digital system driven by smart technology to cater to this equation and explore the association and causation between patient case capturing of “Angina Pectoris” in “Cardio Vascular Diseases (CVD)” and its subsequent treatment selection process with the help of Machine Learning. We have present a methodology and approach roadmap driven by Analytics and Data science for handshaking between medical paradigms of CVD type of diseases, specifically patients suffering from angina pectoris, and Smart Technology to enhance the prediction of medicine for the physicians in order to achieve faster and quicker throughput time for patients.
Chandan Pan, Debasmita Chatterjee *, Ajoy Kumar Ray, Shubabrata Sinha, Rathin Chakravarty, Satadal Das and Krishnendu Paira
Heritage Institute of Technology, Chowbaga Road, Anandapur, East Kolkata Townshio, Kolkata, India.
12 March 2021
15 May 2021
02 June 2021
01 January 2022