Heart Disease Prediction using Machine Learning Techniques
| العنوان: | Heart Disease Prediction using Machine Learning Techniques |
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| المؤلفون: | Samir B. Patel, Santosh Kumar Bharti, Devansh Shah |
| المصدر: | SN Computer Science. 1 |
| بيانات النشر: | Springer Science and Business Media LLC, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Health professionals, Heart disease, business.industry, Computer science, Decision tree, Disease, Machine learning, computer.software_genre, medicine.disease, Domain (software engineering), Random forest, Naive Bayes classifier, ComputingMethodologies_PATTERNRECOGNITION, Health care, medicine, Artificial intelligence, business, computer |
| الوصف: | Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyse huge complex medical data, helping healthcare professionals to predict heart disease. This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naive Bayes, decision tree, K-nearest neighbor, and random forest algorithm. It uses the existing dataset from the Cleveland database of UCI repository of heart disease patients. The dataset comprises 303 instances and 76 attributes. Of these 76 attributes, only 14 attributes are considered for testing, important to substantiate the performance of different algorithms. This research paper aims to envision the probability of developing heart disease in the patients. The results portray that the highest accuracy score is achieved with K-nearest neighbor. |
| تدمد: | 2661-8907 2662-995X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::132084ec996d92ea3daada30ed767a68 https://doi.org/10.1007/s42979-020-00365-y |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi...........132084ec996d92ea3daada30ed767a68 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 26618907 2662995X |
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