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  1. Prediction of Heart Disease Using a Collection of Machine and Deep Learning Algorithms.Ali M. A. Barhoom, Abdelbaset Almasri, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):1-13.
    Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...)
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  2. Prediction of Heart Disease Using a Collection of Machine and Deep Learning Algorithms.Ali M. A. Barhoom, Abdelbaset Almasri, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (4):1-13.
    Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...)
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  3. Exploring Machine Learning Techniques for Coronary Heart Disease Prediction.Hisham Khdair - 2021 - International Journal of Advanced Computer Science and Applications 12 (5):28-36.
    Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the occurrence of CHD events (...)
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  4. Prognostic System for Heart Disease Using Machine Learning: A Review.R. Senthilkumar - 2021 - Journal of Science Technology and Research (JSTAR) 2 (1):33-38.
    In today’s world it became difficult for daily routine check-up. The Heart disease system is an end user support and online consultation project. Here the motto behind it is to make a person to know about their heart related problem and according to it formulate them how much vital the disease is. It will be easy to access and keep track of their respective health. Thus, it’s important to predict the disease as earliest. Attributes such as Bp, Cholesterol, Diabetes are (...)
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  5. A Conceptual Model: The Impact of Sociodemographic on Coronary Heart Disease.Aza Azlina Md Kassim, Juliza Mohamed & Zainal Azhar Zainal Azim - 2020 - International Journal of Scientific Research and Management (IJSRM) 8 (1).
    The paper aims to examine the conceptual model on the impact of sociodemographic towards coronary heart disease. There is still limited study on the association between cost of living particularly the cost of food and housing and its impact on coronary heart disease. Therefore, this study incorporates five essential sociodemographic attributes namely, cost of food, children education cost, housing cost, smoking habit and gender. The study provides greater understanding on how the cost of living and lifestyle may give impact to (...)
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  6. The Effect of Evoking Nostalgic Memories on the Homeostatic Variables (Mental and Physical) Among Cardiovascular Patients.Hossein Dabbagh - 2018 - Advances in Cognitive Science 19 (4):57-69.
    Nostalgia as one of the complex emotions has been challenged over the past few decades due to its psychological and physiological functions. The present experiment investigates the effect of recalling nostalgic memories on amelioration of homeostatic and health state of people with cardiovascular disease. Method: The participants were 30 patients who were hospitalized for angiography procedure. The research was based on an experimental design with randomized and post-test groups. The instruments used included a thermometer with ° C, a checkout manipulation (...)
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