[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118361-en":3,"doc-seo-118361-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118361,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",7,"Healthcare","The Impact of Ontology on the Prediction of Cardiovascular Disease Compared to Machine Learning Algorithms","Cardiovascular disease is a rising chronic condition whose complications often result from late or incorrect detection. The study develops and evaluates an ontology-based machine learning approach and compares it with widely used classifiers to build an automated system for heart-disease identification. The comparison covers Random Forest, Logistic Regression, Decision Tree, Naive Bayes, k-Nearest Neighbours, Artificial Neural Network, and Support Vector Machine using a dataset of 70,000 instances. Performance is assessed via confusion-matrix-derived metrics including F-Measure, Accuracy, Recall, and Precision. Results indicate ontology-based methods outperform all tested machine learning algorithms.","The Impact of Ontology on the Prediction of Cardiovascular Disease Compared to Machine Learning  \nAlgorithms  \n[https://doi.org/10.3991/ijoe.v18i11.32647](https://doi.org/10.3991/ijoe.v18i11.32647)  \nHakim El Massari1(􀀍 ), Noreddine Gherabi1, Sajida Mhammedi1, Hamza Ghandi1, Mohamed Bahaj2, Muhammad Raza Naqvi3  \n1 National School of Applied Sciences, Sultan Moulay Slimane University, Beni-Mellal,  \nMorocco  \n2 Faculty of Sciences and Technologies, Hassan First University, Settat, Morocco  \n3 National Engineering School of Tarbes INP-ENIT, University of Toulouse, Toulouse, France h .elmassari@usms .ma  \nAbstract—Cardiovascular disease is one of the chronic diseases that is on the rise. The complications occur when cardiovascular disease is not discovered early and correctly diagnosed at the right time. Various machine learning approaches, including ontology-based Machine Learning techniques, have lately played an essential role in medical science by building an automated system that can identify heart illness. This paper compares and reviews the most prominent machine learning algorithms, as well as ontology-based Machine Learning classification. Random Forest, Logistic regression, Decision Tree, Naive Bayes, k-Nearest Neighbours, Artificial Neural Network, and Support Vector Machine were among the classification methods explored. The dataset used consists of 70000 instances and can be downloaded from the Kaggle website. The findings are assessed using performance measures generated from the confusion matrix, such as F-Measure, Accuracy, Recall, and Precision. The results showed that the ontology outperformed all the machine learning algorithms.  \nKeywords—cardiovascular, ontology, swrl, prediction, machine learning  \n1 Introduction  \nCardiovascular diseases (CVD) are a group of disorders affecting the heart and blood vessels. According to the World Health Organization (WHO), CVD is the leading cause of death in the world more people die each year from CVD than from anyother cause. An estimated 17.7 million deaths are attributable to CVD, representing 31% of total global mortality. Given these statistical numbers, it is important to reveal cardiovascular disease as early as possible with the help of the trending technology based on machine learning and ontology, so that management with assistance and medicines can begin.  \nMachine learning (ML) is one of the most constantly evolving areas of computer science, with a wide range of applications. It is the process of obtaining usable infor-  \nmation from a big quantity of data. Medical diagnosis, marketing, industry, and other scientific domains all make use of ML approaches. ML algorithms are well-suited for medical data analysis since they have been frequently employed in medical datasets. ML comes in several forms, including classification, regression, and clustering. Each form has a particular consequence and influence depending on the problem that weare attempting to address. We focus on classification algorithms in our work because of their high accuracy and performance in classifying a given dataset into predetermined categories and predicting future events or information from that data. In the medical field, classification algorithms are often utilized, particularly in the diagnosis of illnesses such as cardiovascular disease. Therefore, the commonly used machine learning classification [1] namely SVM, NB, DT, KNN, ANN, and LR are applied to identify patients with cardiovascular disease at an early period.  \nOn the other hand, ontology has been one of the most widely used techniques to managing, organizing, and extracting data during the last few decades. It is a way of data representation that has been effectively utilized in a number of domains, particularly the medical domain. It is significant in computer science because of its ability to express many concepts and their relationships across fields. In reality, no single ontology is sufficient to meet today's expanding","cbCaidcBsgNPkndA","https://ap.wps.com/l/cbCaidcBsgNPkndA","pdf",1160537,1,15,"English","en",105,"# Introduction\n## Cardiovascular diseases and early detection\n## Machine learning for classification\n## Ontology for data representation\n## Paper structure\n# Literature review\n## Prior machine learning approaches for risk detection\n## Examples of reported predictive performance","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses early and correct prediction of cardiovascular disease to reduce complications caused by delayed or inaccurate diagnosis.\"},{\"question\":\"Which machine learning algorithms are compared in the study?\",\"answer\":\"The study compares Random Forest, Logistic Regression, Decision Tree, Naive Bayes, k-Nearest Neighbours, Artificial Neural Network, and Support Vector Machine.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Performance is measured using confusion-matrix-derived metrics including F-Measure, Accuracy, Recall, and Precision.\"}]","The Impact of Ontology on the Prediction of Cardiovascular Disease Compared to Machine Learning Algorithms | 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