Performance evaluation of machine learning models to predict heart

• Autorzy: Khan Majid , Husnain Ghassan , Ahmad Waqas , Shaukat Zain , Jan Latif , Haq Ihtisham Ul , Ishtiaq Atif

Identyfikatory

DOI

10.22630/MGV.2023.32.1.6

• Języki publikacji: EN

Abstrakty

EN

Coronary Artery Disease is the type of cardiovascular disease (CVD) that happens when the blood vessels which stream the blood toward the heart, either become tapered or blocked. Of this, the heart is incapable to push sufficient blood to encounter its requirements. This would lead to angina (chest pain). CVDs are the leading cause of mortality worldwide. According to WHO, in the year 2019 17.9 million people deceased from CVD. Machine Learning is a type of artificial intelligence that uses algorithms to help analyse large datasets more efficiently. It can be used in medical research to help process large amounts of data quickly, such as patient records or medical images. By using Machine Learning techniques and methods, scientists can automate the analysis of complex and large datasets to gain deeper insights into the data. Machine Learning is a type of technology that helps with gathering data and understanding patterns. Recently, researchers in the healthcare industry have been using Machine Learning techniques to assist with diagnosing heart-related diseases. This means that the professionals involved in the diagnosis process can use Machine Learning to help them figure out what is wrong with a patient and provide appropriate treatment. This paper evaluates different machine learning models performances. The Supervised Learning algorithms are used commonly in Machine Learning which means that the training is done using labelled data, belonging to a particular classification. Such classification methods like Random Forest, Decision Tree, K-Nearest Neighbour, XGBoost algorithm, Naïve Bayes, and Support Vector Machine will be used to assess the cardiovascular disease by Machine Learning.

Słowa kluczowe

EN

cardiovascular disease; machine learning; heart attack; prediction

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2023
• Tom: Vol. 32, No. 1
• Strony: 99--114
• Opis fizyczny: Bibliogr. 25 poz., rys., tab., wykr.

Twórcy

autor

Khan Majid

• Department of Computer Science, Iqra National University Peshawar, 25100, Pakistan

autor

Husnain Ghassan

• Department of Computer Science, Iqra National University Peshawar, 25100, Pakistan

• https://orcid.org/0000-0001-6637-6754

autor

Ahmad Waqas

• Department of Computer Science, Iqra National University Peshawar, 25100, Pakistan

autor

Shaukat Zain

• Department of Computer Science, Iqra National University Peshawar, 25100, Pakistan

autor

Jan Latif

• Department of Computer Science, Iqra National University Peshawar, 25100, Pakistan

autor

Haq Ihtisham Ul

• Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar, 25100, Pakistan

autor

Ishtiaq Atif

• Department of Computer Science, Iqra National University Peshawar, 25100, Pakistan

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).