A Prediction of Heart Disease Using Machine Learning Algorithms
Now a day’s heart disease is emerging as one of the most death-dealing diseases. As per a report published by the World Health Organization [WHO], heart disease is one of the most hazardous diseases to human which causes death all over the world from the
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oduction In the world full of enlarged and enhanced computer technologies one of the major subfield of computer science that is Artificial Intelligence (Machine Learning and Deep Learning) is used in the medical field to pull out the predictions whether heart disease exists or not based on extracted medical records (image file or .csv file) of the patients from the medical databases called Electronic Health Record with the use of various algorithms [1, 17]. Nowadays heart disease is the most death-dealing disease. As per a report published by the World Health Organization [WHO], heart disease is one of the most hazardous diseases to human which causes death all over the world from the last 20 years. Millions of human beings around the world are suffering from heart disease. Approx. 12 million people dying every year which makes it the biggest challenge for medical professionals how important the early diagnosis of heart disease with better accuracy [2]. There are many traditional methods for predicting such illness but they are not looking sufficient, like data mining algorithms do not predict heart disease with so much accuracy like the machine learning algorithms do (support vector machine, logistic regression, naïve bayes, random forest, and decision tree). In terms of data mining, when we work with these types of algorithms the problem arises from the very © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. I.-Z. Chen et al. (Eds.): ICIPCN 2020, AISC 1200, pp. 497–504, 2021. https://doi.org/10.1007/978-3-030-51859-2_45
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first step called data extraction like incomplete data, missing values, and inconsistency, and predicted results are not so much accurate. Medical industries much needed that type of diagnosis system which can predict heart disease at an early stage and offers more and more accurate diagnosis than traditional methods [3, 18]. After the promising success of machine learning algorithms in various real-life field industries, we have also observed that it can be a promising solution with the highest accuracy for medical diagnosis and it can be seen as a key application in the healthcare industry [4, 17]. In this paper, we are applying machine learning algorithms and comparing their accuracy for classifying whether an algorithm has a more accurate percentage and on this basis, we proposed a modified algorithm for predicting heart disease on various attributes such as age, blood pressure, chest pain, serum cholesterol levels, heart rate, and other characteristic attributes, and the patient will be classified according to varying degrees of coronary artery disease. In this paper, we used the UCI machine learning dataset of 304 patients which contain 304 rows and 14 columns.
2 Related Works Many researchers are continuously working in the field of heart disease prediction to find out better and better accuracy with the use of various algorithms [5]. From the literature survey of different numbers of researchers, various techn
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