[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121760-en":3,"doc-seo-121760-105":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},121760,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Heart disease detection based on machine learning algorithms - Graduation Thesis","Cardiovascular diseases are the leading cause of death worldwide, and early identification of high-risk patients enables timely intervention to reduce premature mortality. This thesis applies artificial intelligence with machine learning to predict risky situations related to heart disease using a public UCI dataset. Multiple predictive models are implemented and tested, including Neural Network, Support Vector Machine, Decision Tree, Naive Bayes, Logistic Regression, and Stochastic Gradient Descent. A performance comparison evaluates accuracy and classification effectiveness, with the Naive Bayes model achieving the best results.","Università degli Studi di Padova Dipartimento di Ingegneria dell’Informazione  \nCorso di Laurea Triennale in Ingegneria Informatica  \nHeart disease detection based on machine learning algorithms  \nSupervisor:  \nProf. Gloria Beraldo  \nStudent:  \nElisa Borella 2007963  \nAcademic Year 2022/2023  \nGraduation Date 29/09/2023  \nTo my grandfather  \nAbstract  \nCardiovascular diseases are the first cause of death all over the world. By using artificial intelligence algorithms and, in particular, machine learning approaches it is possible to predict risky situations due to heart disease. Various approaches are investigated in this thesis such as Neural Network, Support Vector Machine, Decision Tree, Naive Bayes, Logistic Regression and Stochastic Gradient Descent to extract predictive models in order to test for the presence or absence of heart disease.  \nThanks to the public dataset from UCI, it is possible to take advantage of medical data to train the proposed models.  \nA comparison among the different approaches based on the performance was included in this thesis.  \nThe tests of the proposed models revealed performances in terms of accuracy in the range 77%-90 .6% . The Naive Bayes model has been the model with the highest accuracy (90.6%), highest precision (96.4%) and shortest time for classification (0.003 seconds) .  \nii  \nContents  \n1 Introduction 1  \n1.1 Motivations .............................. 1  \n1.2 Related work ............................. 2  \n1.3 Aim of this thesis ........................... 4  \n1.4 Thesis’ structure ........................... 4  \n2 Methods 7  \n2.1 Dataset description and data preprocessing ............. 7  \n2.1.1 UCI Dataset Analysis ..................... 10  \n2.1.2 Data preprocessing ...................... 17  \n2.2 Experimental setup .......................... 18  \n2.3 Training ................................ 19  \n2.3.1 Hyperparameter tuning .................... 19  \n2.3.2 K-fold cross-validation .................... 19  \n2.4 Metrics ................................. 20  \n3 Models 23  \n3.1 Neural Network ............................ 23  \n3.1.1 Algorithm implementation .................. 23  \n3.2 Support Vector Machine ....................... 25  \n3.2.1 Algorithm implementation .................. 25  \n3.3 Naive Bayes Classifier ........................ 26  \n3.3.1 Algorithm implementation .................. 26  \n3.4 Stochastic Gradient Descent ..................... 27  \n3.4.1 Algorithm implementation .................. 27  \n3.5 Decision Tree Classifier ........................ 27  \n3.5.1 Algorithm implementation .................. 27  \n3.6 Logistic Regression .......................... 28  \n3.6.1 Algorithm implementation .................. 28  \n4 Results 29  \n4.1 Performances ............................. 29  \n4.1.1 Confusion Matrices ...................... 30  \n4.2 Discussion ............................... 33  \nConclusions and future work 35  \nBibliography 37  \nAcknowledgments 41  \nChapter 1  \nIntroduction  \nThis chapter presents the reasons that led to the writing of this thesis, the purposes of this work and this thesis structure. Section 1 .2 is dedicated to present related works about heart disease detection with machine learning.  \n1.1 Motivations  \nThe World Health Organization declares that cardiovascular diseases (CVDs) are the main cause of death in the whole world, having caused 17.9 million deaths in 20191. This type of diseases comprehend coronary heart disease, cerebrovascular disease, rheumatic heart disease and others. Most of them could be prevented by avoiding risk behaviour, such as abuse of alcohol, tobacco, unhealthy food. The effects of CVDs might appear in the human body via high blood pressure, high blood glucose, high blood lipids. It is crucial to detect these pathologies in time, giving appropriate treatment to the patients with the aim of avoiding premature deaths. Artificial Intelligence can be helpful to identify which patients are at high risk of cardiovascular event","cbCaieUCYneSaBsj","https://ap.wps.com/l/cbCaieUCYneSaBsj","pdf",1806130,1,49,"English","en",105,"# Introduction\n## Motivations\n## Related work\n## Aim of this thesis\n## Thesis’ structure\n# Methods\n## Dataset description and data preprocessing\n## Experimental setup\n## Training\n## Metrics\n# Models\n## Neural Network\n## Support Vector Machine\n## Naive Bayes Classifier\n## Stochastic Gradient Descent\n## Decision Tree Classifier\n## Logistic Regression\n# Results\n## Performances\n## Discussion\n# Conclusions and future work","[{\"question\":\"Which machine learning models are used for heart disease prediction in this thesis?\",\"answer\":\"The thesis investigates Neural Network, Support Vector Machine, Decision Tree, Naive Bayes, Logistic Regression, and Stochastic Gradient Descent to build predictive models.\"},{\"question\":\"What dataset is used to train and evaluate the models?\",\"answer\":\"The models are trained using the public dataset from UCI, leveraging medical data to support classification.\"},{\"question\":\"How does the performance of the models compare, and which one performs best?\",\"answer\":\"Models are compared by performance metrics such as accuracy, precision, and classification time. The Naive Bayes model achieves the highest accuracy (90.6%), highest precision (96.4%), and the shortest classification time (0.003 seconds).\"}]","Heart disease detection based on machine learning algorithms - Graduation Thesis | PDF",1785806692,123,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"heart-disease-detection-based-on-machine-learning-algorithms-graduation-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/heart-disease-detection-based-on-machine-learning-algorithms-graduation-thesis/121760/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are used for heart disease prediction in this thesis?","Question",{"text":75,"@type":76},"The thesis investigates Neural Network, Support Vector Machine, Decision Tree, Naive Bayes, Logistic Regression, and Stochastic Gradient Descent to build predictive models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset is used to train and evaluate the models?",{"text":80,"@type":76},"The models are trained using the public dataset from UCI, leveraging medical data to support classification.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the performance of the models compare, and which one performs best?",{"text":84,"@type":76},"Models are compared by performance metrics such as accuracy, precision, and classification time. 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