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The work imports a dataset with required Python and machine learning libraries, performs data understanding, and runs preprocessing using techniques such as correlation analysis and visualizations to standardize and scale features. Multiple classifiers are then trained and evaluated by their accuracy, enabling identification of the best-performing algorithm for the given dataset.","CALIFORNIA STATE UNIVERSITY, NORTHRIDGE  \nHeart Disease Prediction using Machine Learning algorithms  \nA thesis submitted in partial fulfillment of the requirements For the degree of Master of Science In Computer Science  \nBy  \nYash Sharma  \nThe thesis of Yash Sharma is approved:  \nDr. Mahdi Ebrahimi Date  \nDr. Robert McIlhenny  \nDate  \n_______________________________________ ________________  \nDr. Taehyung Wang, Chair Date  \nCalifornia State University, Northridge  \nAcknowledgements  \nI would like to thank Dr. George Wang for being my committee chair for my thesis and for all his help throughout the entire year. I would also like to thank Dr. Mahdi Ebrahimi for considering becoming part of my committee this semester and Dr. Robert McIlhenny for accepting my request to be on my committee this semester.  \nTable of Contents  \nSignature Page……………………………………………………………………………………ii  \nAcknowledgements………………………………………………………………………………iii  \nList of Tables……………………………………………………………………………………..vi  \nList of Figures…………………………………………………………………………………….vi  \nAbstract…………………………………………………………………………………………..vii  \n1. Introduction……………………………………………………………………………………1  \n1.1. Objective…………………………………………………………………………………1  \n1.2. Problem Statement……………………………………………………………………….2  \n1.3. Target……………………………………………………………………………………..4  \n1.4. Planning (Updated timeline & issues encountered)……………………………………...4  \n2. Survey and Background……………………….....……………………………………………7  \n2.1. Survey…………………………………………………………………………………….7  \n2.2. Background…………………………………………………………………………….....9  \n2.3. Related Work…………………………………………………………………………....11  \n3. Project Development and design…………………………………………………………….13  \n3.1. Technical Approach & Python libraries Discussion…………………………………….13  \n3.2. Data Understanding and Preprocessing…………………………………………………14  \n3.3. Decision Tree Classifier Overview……………………………………………………...16  \n3.4. K-Neighbors Classifier Overview………………………………………………………18  \n3.5. Random Forest Classifier Overview…………………………………………………….20  \n3.6. Support Vector Classifier Overview…………………………………………………….21  \n3.7. Risks involved…………………………………………………………………………..25  \n4. Results………………………………………………………………………………………..26  \n4.1. Decision Tree Classifier results………………………………………………………....26  \n4.2. K-Neighbors Classifier results…………………………………………………………..27  \n4.3. Random Forest Classifier results………………………………………………………..28  \n4.4. Support Vector Classifier results………………………………………………………..29  \n4.5. Result Analysis...………………………………………………………………………..31  \n5. Conclusion and Future Work………………………………………………………………...33  \n6. Bibliography…………………………………………………………………………………35  \nList of Tables  \n1. Table 1.1 Revised timeline for the project………………………………………………….5-6  \nList of Figures  \n1. Figure 2.1 Examining Kaggle and IEEE dataset…………………………………………….15  \n2. Figure 2.2 Generalized layout ofDecision Tree………………………………..……………17  \n3. Figure 2.3 K-Neighbors Classifier Diagram…………………………………………………19  \n4. Figure 2.4 Random Forest Classifier using Decision Trees…………………………………20  \n5. Figure 2.5 Optimal hyperplane selection graphs for Support Vector Classifier…………21-22  \n6. Figure 2.6 Linear Kernel……………………………………………………………………..23  \n7. Figure 2.7 Polynomial Kernal………………………………………………………………..23  \n8. Figure 2.8 Radial Basis Function Kernal…………………………………………………….24  \n9. Figure 2.9 Decision Tree Classifier Result Graph…………………………………………...26  \n10. Figure 2.10 K-Neighbors Classifier Result Graph…………………………………………...27  \n11. Figure 2.11 Random Forest Classifier Result Graph………………………………………...28  \n12. Figure 2.12 Support Vector Classifier Result Graph………………………………………...30  \nAbstract  \nHeart Disease Prediction using Machine Learning algorithms  \nBy  \nYash Sharma  \nMaster of Science in Computer Science  \nIn a rapidly evolving world, humans deal with numerous challenges that can range from learning how to talk and walk to leading a nation or fighting enemies and, simply, anything that a mind can imagine. With such a variety of issues that every human deals with regularly, they naturally try to consider easy soluti","cbCaifJ3K6iQQlPI","https://ap.wps.com/l/cbCaifJ3K6iQQlPI","pdf",625120,1,46,"English","en",105,"# Introduction\n## Objective\n## Problem Statement\n## Target\n## Planning (Updated timeline & issues encountered)\n# Survey and Background\n## Survey\n## Background\n## Related Work\n# Project Development and design\n## Technical Approach & Python libraries Discussion\n## Data Understanding and Preprocessing\n## Decision Tree Classifier Overview\n## K-Neighbors Classifier Overview\n## Random Forest Classifier Overview\n## Support Vector Classifier Overview\n## Risks involved\n# Results\n## Decision Tree Classifier results\n## K-Neighbors Classifier results\n## Random Forest Classifier results\n## Support Vector Classifier results\n## Result Analysis\n# Conclusion and Future Work\n# Bibliography","[{\"question\":\"Why is early heart disease detection a key focus of this thesis?\",\"answer\":\"Heart disease can develop from coronary artery plaque and blockage and may be hard to identify early. The thesis aims to reduce risk of severe outcomes by enabling early prediction using a machine learning model.\"},{\"question\":\"What preprocessing steps are applied to the dataset?\",\"answer\":\"The dataset is imported with the necessary Python libraries, then understood and preprocessed. The process includes using tools such as a correlation matrix and bar plots, followed by standardization and scaling for optimal model performance.\"},{\"question\":\"How does the thesis decide which machine learning algorithm performs best?\",\"answer\":\"After preprocessing, multiple classifiers are applied and each model’s accuracy is evaluated. The algorithm with the highest accuracy on the dataset is identified as providing the best results.\"}]","Heart Disease Prediction using Machine Learning algorithms - Master’s Thesis | PDF",1785815994,116,{"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-prediction-using-machine-learning-algorithms-masters-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-prediction-using-machine-learning-algorithms-masters-thesis/123335/",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},"Why is early heart disease detection a key focus of this thesis?","Question",{"text":75,"@type":76},"Heart disease can develop from coronary artery plaque and blockage and may be hard to identify early. The thesis aims to reduce risk of severe outcomes by enabling early prediction using a machine learning model.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What preprocessing steps are applied to the dataset?",{"text":80,"@type":76},"The dataset is imported with the necessary Python libraries, then understood and preprocessed. The process includes using tools such as a correlation matrix and bar plots, followed by standardization and scaling for optimal model performance.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis decide which machine learning algorithm performs best?",{"text":84,"@type":76},"After preprocessing, multiple classifiers are applied and each model’s accuracy is evaluated. 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