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Cardiovascular risk assessment using data mining inferencing and feature engineering techniques

التفاصيل البيبلوغرافية
العنوان: Cardiovascular risk assessment using data mining inferencing and feature engineering techniques
المؤلفون: Siddharth Swarup Rautaray, Aanchal Sahu, Harshvardhan Gm, Manjusha Pandey, Mahendra Kumar Gourisaria
المصدر: International Journal of Information Technology. 13:2011-2023
بيانات النشر: Springer Science and Business Media LLC, 2021.
سنة النشر: 2021
مصطلحات موضوعية: Feature engineering, Soft computing, Computer Networks and Communications, Computer science, Applied Mathematics, Decision tree, 020206 networking & telecommunications, 02 engineering and technology, computer.software_genre, Logistic regression, Computer Science Applications, Random forest, Support vector machine, Naive Bayes classifier, ComputingMethodologies_PATTERNRECOGNITION, Computational Theory and Mathematics, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Data mining, Electrical and Electronic Engineering, Risk assessment, computer, Information Systems
الوصف: With the frequent decline in people’s health due to the hectic lifestyle, increased levels of workload and intake of fast food, there has been an unfortunate growth in the number of patients suffering from cardiovascular diseases each year. Around the world, millions of people die each year due to cardiovascular diseases. While the statistics are eye-opening, with the vast amount of data about heart patients in our hands, we can save millions by detecting the risk at an early stage. With the recent advances in soft computing and fuzzy logic, various algorithmic approaches are employed to tackle the issue of cardiovascular risk assessment through machine learning. Using some of the algorithms of machine learning like Logistic Regression (LR), Naive Bayes (NB), Support vector machine (SVM), and Decision tree (DT), Random Forest (RF) and K-Nearest Neighbours (KNN) classifiers, a model can be built to predict the risk accurately. In this paper, we have analysed each of the above methods normally and through feature engineering techniques like transformation through Principal Component Axes and considering different train-test folds to find the best performing model, which was found to be KNN in terms of all metrics and Logistic Regression in terms of accuracy.
تدمد: 2511-2112
2511-2104
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::b2b5f9cd20ba9aa0f9cf012a827e58de
https://doi.org/10.1007/s41870-021-00650-w
حقوق: CLOSED
رقم الأكسشن: edsair.doi...........b2b5f9cd20ba9aa0f9cf012a827e58de
قاعدة البيانات: OpenAIRE
الوصف
تدمد:25112112
25112104