[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121414-en":3,"doc-seo-121414-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},121414,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Enhanced Machine Learning Model for CVD Prediction using Principal Component Analysis (PCA)","World Health Organization (WHO) reports cardiovascular diseases as the leading cause of about 17.9 million deaths annually, with especially severe impact in low- and middle-income countries due to limited early screening and targeted treatment. Faster detection of heart attacks can reduce mortality risk. Using the Cleveland Heart Disease dataset from UCI, the study standardizes features, applies Principal Component Analysis (PCA), and performs hyperparameter tuning across SVM, KNN, Logistic Regression, and Multi-Layer Perceptron, combined via a Voting Classifier. The hybrid model achieves 98.33% accuracy and strong precision, F1, and recall, with small train-test metric gaps. Results show PCA-assisted hybrid modeling improves accuracy while reducing dimensionality.","Enhanced Machine Learning Model for CVD Prediction using Principal Component Analysis (PCA)  \nShailendra Chaurasia 1 and A. K Sachan2  \n(IJGASR) International Journal For Global Academic & Scientific Research ISSN Number: 2583-3081  \nVolume 4, Issue No. 2, 22–45 © The Authors 2025  \n[journals.icapsr.com/index.php/ijgasr](journals.icapsr.com/index.php/ijgasr)  \nDOI: 10.55938/ijgasr.v4i2.202  \nAbstract  \nThe World Health Organization (WHO) report says that each year, cardiovascular diseases are the leading reason for around 17.9 million deaths across the globe. This is a more serious problem in lowand middle-income countries where there are barriers to early check-ups and specific treatments. The quicker and better detection of heart attacks helps reduce the risk of death. Based on previous methods, the study takes the Cleveland Heart Disease dataset from the UCI Machine Learning repository and uses it to design and check best machine learning models that take advantage of standardization, Principal Component Analysis (PCA) and hyperparameter tuning. We used machine learning algorithms such as Support Vector Machine, k-Nearest Neighbors, Logistic Regression and a Multi Layer Perceptron model, all combined under a Voting Classifier. With a 98.33% accuracy, 98.25% F1-score, 96.55% precision, and 100% recall on test data, the enhanced hybrid model (Voting Classifier) leaves all other models far behind in performance. The hybrid model had small gaps between train and test values for metrics, with 1.24% accuracy difference, 1.29% F1-score difference, 3.45% precision difference and-0.92% recall difference. Incorporating Principal Component Analysis (PCA) lowered the number of dimensions used while increasing accuracy, precision and F1 scores for a number of models. The results suggest that the use of Principal Component Analysis (PCA)-combined hybrid models leads to better, more understandable and trustworthy tools for predicting CVD. Strengthening predictive models for CVD risk assessment is now possible, supporting prompt clinical choices and helping patients improve.  \nKeywords  \nWHO, Heart Disease, Cardiovascular Disease (CVD, UCI Dataset, Machine Learning, PCA, Feature Selection, SVM (Support Vector Machine), KNN (K-Nearest Neighbors), LR (Logistic Regression) And A MLP (Multi Layer Perceptron), Hybrid Model (Voting Classifier)  \nReceived: 26 May 2025; Revised: 03 July 2025; Accepted: 08 July 2025; Published: 15 July 2025 Introduction  \nCardiovascular diseases consist of several heart and blood vessel illnesses, including coronary artery disease, stroke and heart failure, all of which significantly threaten worldwide health. Cardiovascular diseases are the leading cause of deaths across the globe. [15],[20] The World Health Organization (WHO)  \n1,2Department of Computer Science Engineering, LNCT University, Bhopal Madhya Pradesh, India. chaurasia.shailendra@gmail. com, sachank_ [12@yahoo.com](12@yahoo.com)  \nCorresponding Author:  \nE-mail: [chaurasia.shailendra@gmail.com](chaurasia.shailendra@gmail.com)  \n© 2025 by Shailendra Chaurasia and A.K Sachan Submitted for possible open access publication under the terms  \nand conditions of the Creative Commons Attribution (CC BY) license,([http://creativecommons.org/licenses/](http://creativecommons.org/licenses/)[ ](http://creativecommons.org/licenses/)by/4.0/). This work is licensed under a Creative Commons Attribution 4.0 International License  \nsays that almost 18 million people lost their lives to cardiovascular diseases in 2019 (32% of all deaths worldwide) . [1] The World Heart Federation found that each year, an estimated number of deaths caused by heart disease rose from 12.1 in 1990 to 18.6 in 2019, owing to an expanding population with risk factors. Areport by the World Heart Federation states that 80% of the deaths from cardiovascular diseases are premature.[25] Heartattacks and strokes could be prevented if risk management is done promptly, yet in reality, it can be difficul","cbCaiiGNg6c1klUF","https://ap.wps.com/l/cbCaiiGNg6c1klUF","pdf",1956164,1,24,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Background and public health burden\n## Role of machine learning in early detection\n## Limitations of prior models and datasets\n## Prior related work and study objective","[{\"question\":\"What problem does this study address in cardiovascular disease (CVD) prediction?\",\"answer\":\"It targets improving early and accurate detection of cardiovascular disease risk, aiming to support timely clinical choices and reduce death risk.\"},{\"question\":\"Which dataset and preprocessing approach are used for the model development?\",\"answer\":\"The study uses the Cleveland Heart Disease dataset from the UCI Machine Learning repository and incorporates standardization along with Principal Component Analysis (PCA) for dimensionality reduction.\"},{\"question\":\"How is the final model constructed and what performance is reported?\",\"answer\":\"It combines SVM, k-Nearest Neighbors, Logistic Regression, and a Multi Layer Perceptron under a Voting Classifier. On test data, it reports about 98.33% accuracy with corresponding high F1-score, precision, and recall.\"}]","Enhanced Machine Learning Model for CVD Prediction using Principal Component Analysis (PCA) | PDF",1785735560,60,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"enhanced-machine-learning-model-for-cvd-prediction-using-principal-component-analysis-pca","",{"@graph":36,"@context":86},[37,54,69],{"@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/enhanced-machine-learning-model-for-cvd-prediction-using-principal-component-analysis-pca/121414/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does this study address in cardiovascular disease (CVD) prediction?","Question",{"text":76,"@type":77},"It targets improving early and accurate detection of cardiovascular disease risk, aiming to support timely clinical choices and reduce death risk.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which dataset and preprocessing approach are used for the model development?",{"text":81,"@type":77},"The study uses the Cleveland Heart Disease dataset from the UCI Machine Learning repository and incorporates standardization along with Principal Component Analysis (PCA) for dimensionality reduction.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the final model constructed and what performance is reported?",{"text":85,"@type":77},"It combines SVM, k-Nearest Neighbors, Logistic Regression, and a Multi Layer Perceptron under a Voting Classifier. On test data, it reports about 98.33% accuracy with corresponding high F1-score, precision, and recall.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":29,"slug":109},5,"Comic","comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]