[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119187-en":3,"doc-seo-119187-105":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},119187,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","IMPROVING HEART DISEASE PREDICTION ACCURACY USING PRINCIPAL COMPONENT ANALYSIS (PCA) IN MACHINE LEARNING ALGORITHMS","This study improves heart disease prediction accuracy by applying Principal Component Analysis (PCA) for feature extraction across multiple machine learning algorithms. The dataset includes 334 records with 49 attributes, covering 5 classes and 31 target diagnoses. Five models are evaluated: K-nearest neighbors (KNN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT). Results indicate that PCA-enhanced models achieve high accuracy, with RF, LR, and DT reaching up to 1.00. The findings support PCA-based approaches for early heart disease diagnosis.","IMPROVING HEART DISEASE PREDICTION ACCURACY USING PRINCIPAL COMPONENT ANALYSIS (PCA) IN MACHINE LEARNING ALGORITHMS  \nZirji Jayidan1, Amril Mutoi Siregar*2, Sutan Faisal3, Hanny Hikmayanti4  \n1,2,3,4Informatics Departement, Faculty of Computer Sciences, Universitas Buana Perjuangan Karawang,  \nIndonesia  \n[Email:](Email:1  if20.zirjijayidan@mhs.ubpkarawang.ac.id)[1 ](Email:1  if20.zirjijayidan@mhs.ubpkarawang.ac.id)[ if20.zirjijayidan@mhs.ubpkarawang.ac.id](Email:1  if20.zirjijayidan@mhs.ubpkarawang.ac.id), [2](2amril.mutoi@ubpkarawang.ac.id)[amril.mutoi@ubpkarawang.ac.id](2amril.mutoi@ubpkarawang.ac.id), [3](3sutanfaisal@ubpkarawang.ac.id)[sutanfaisal@ubpkarawang.ac.id](3sutanfaisal@ubpkarawang.ac.id), [4](4hanny.hikmayanti@ubpkarawang.ac.id)[hanny.hikmayanti@ubpkarawang.ac.id](4hanny.hikmayanti@ubpkarawang.ac.id)  \n(Article received: May 06, 2024; Revision: May 25, 2024; published: June 04, 2024)  \nAbstract  \nThis study aims to improve the accuracy of heart disease prediction using Principal Component Analysis (PCA) for feature extraction and various machine learning algorithms. The dataset consists of 334 rows with 49 attributes, 5 classes and 31 target diagnoses. The five algorithms used were K-nearest neighbors (KNN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT). Results show that algorithms using PCA achieve high accuracy, especially RF, LR, and DT with accuracy up to 1.00. This research highlights the potential of PCA-based machine learning models in early diagnosis of heart disease.  \nKeywords: Diagnostic Accuracy, Feature Extraction, Heart Disease Prediction, Machine Learning Algorithm, Principal Component Analysis (PCA).  \nPENINGKATAN AKURASI PREDIKSI PENYAKIT JANTUNG MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS (PCA) PADA ALGORITMA MACHINE  \nLEARNING  \nAbstrak  \nPenelitian ini bertujuan meningkatkan akurasi prediksi penyakit jantung menggunakan Principal Component Analysis (PCA) untuk ekstraksi fitur dan berbagai algoritma machine learning. Dataset terdiri dari 334 baris dengan 49 atribut, 5 kelas dan 31 diagnosis target. Lima algoritma yang digunakan adalah K-nearest neighbors (KNN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), dan Decision Tree (DT) . Hasil menunjukkan bahwa algoritma yang menggunakan PCA mencapai akurasi tinggi, terutama RF, LR, dan DT dengan akurasi hingga 1.00. Penelitian ini menyoroti potensi model machine learning berbasis PCA dalam diagnosis dini penyakit jantung.  \nKata kunci: Akurasi Diagnostik, Algoritma Machine Learning, Ekstraksi Fitur, Prediksi Penyakit Jantung, Principal Component Analysis (PCA).  \n1. PENDAHULUAN  \nMenurut Organisasi Kesehatan Dunia (WHO) pada tahun 2021, diperkirakan pada tahun 2019 dari 17,9 juta kematian di seluruh dunia, 32% dari seluruh kematian di dunia, 85% disebabkan oleh penyakit jantung dan stroke. Dari 17 juta kematian dini (dibawah usia 70 tahun) diakibatkan penyakit tidak menular pada tahun 2019, 38% disebabkan olehpenyakit jantung dan pembuluh darah [1] .  \nMenurut World Heart Federation (WHF), Lebih dari 500 juta orang di seluruh dunia masih terpengaruh oleh penyakit kardiovaskular, yang menyebabkan 20,5 juta kematian pada tahun 2021 [2] .  \nBerdasarkan data dari Global Burden of Disease dan Institute for Health Metrics and Evaluation (IHME) tahun 2014-2019, penyakit jantung adalah penyebab utama kematian di Indonesia. Data dari Badan Riset Kesehatan Dasar (Riskesdas) tahun 2013 dan 2018 menunjukkan adanya peningkatan tren penyakit jantung, yaitu dari 0,5% pada tahun 2013 menjadi 1,5% pada tahun 2018 [3] .  \nOleh karena itu, jika penyakit ini dapat dideteksi sejak dini, dampak buruk yang mungkin timbul dapat segera dicegah. Algoritma klasifikasi machine learning memiliki kemampuan untuk menngetahui cara kerja dan kefektifan fitur Principal Component Analysis (PCA) dari masing-masing algoritma.  \nPrincipal component Analysis (PCA) adalah fitur pengurangan dimensi yang paling","cbCailnuPIE984ah","https://ap.wps.com/l/cbCailnuPIE984ah","pdf",1098117,1,10,"English","en",105,"# Abstract\n# Keywords\n# PENDAHULUAN","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To improve the accuracy of heart disease prediction by using Principal Component Analysis (PCA) for feature extraction together with multiple machine learning algorithms.\"},{\"question\":\"Which machine learning algorithms are evaluated?\",\"answer\":\"K-nearest neighbors (KNN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT).\"},{\"question\":\"What results are achieved when PCA is applied?\",\"answer\":\"PCA-based models show high accuracy, especially Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT), reaching accuracy up to 1.00.\"}]","IMPROVING HEART DISEASE PREDICTION ACCURACY USING PRINCIPAL COMPONENT ANALYSIS (PCA) IN MACHINE LEARNING ALGORITHMS | 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is the main goal of the study?","Question",{"text":75,"@type":76},"To improve the accuracy of heart disease prediction by using Principal Component Analysis (PCA) for feature extraction together with multiple machine learning algorithms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are evaluated?",{"text":80,"@type":76},"K-nearest neighbors (KNN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT).",{"name":82,"@type":73,"acceptedAnswer":83},"What results are achieved when PCA is applied?",{"text":84,"@type":76},"PCA-based models show high accuracy, especially Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT), reaching accuracy up to 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