Design and analysis of an early heart attack detection using openCV

• Main Authors: Muhammad Rafsanjani, Basri, Fahmi, Samsuri
• Format: Conference or Workshop Item
• Language: English
• Published: 2022
• Subjects: T Technology (General); TA Engineering (General). Civil engineering (General); TJ Mechanical engineering and machinery; TK Electrical engineering. Electronics Nuclear engineering; TS Manufactures
• Online Access: http://umpir.ump.edu.my/id/eprint/37128/1/Design%20and%20analysis%20of%20an%20early%20heart%20attack%20detection%20using%20opencv.pdf; http://umpir.ump.edu.my/id/eprint/37128/; https://ncon-pgr.ump.edu.my/index.php/en/?option=com_fileman&view=file&routed=1&name=E-BOOK%20NCON%202022%20.pdf&folder=E-BOOK%20NCON%202022&container=fileman-files
• Institution: Universiti Malaysia Pahang Al-Sultan Abdullah

Millions of people die every year from heart attacks, according to research. The healthcare industry generates massive volumes of data related to heart attacks, but this data is sadly not being processed for hidden insights that could improve decision-making. Early detection of heart attack symptoms is a crucial part of treatment at the moment. Numerous researchers, each applying their own unique machine learning approach, have used the UCI machine learning heart attack dataset. This research aims to detect cardiac events with the use of four different algorithms: logistic regression, decision trees, random forest, and k nearest neighbor using python language. Next, in this project, website prediction of the heart attack prediction are build using python and flask framework. Hyper-parameter tuning method also has been applied to see does the algorithm increase accuracy or not.