[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118493-id":3,"doc-seo-118493-113":31,"detail-sidebar-cat-0-id-113":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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},118493,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",54,"Penelitian & Laporan","Integrasi Model Multiple Machine Learning untuk Memprediksi Resiko Gagal Jantung - Penelitian dan Evaluasi","Penelitian ini bertujuan mengembangkan dan mengevaluasi berbagai model machine learning untuk memprediksi gagal jantung berdasarkan informasi medis pasien. Model yang dibandingkan menggunakan algoritma logistic regression, decision tree, random forest, k-nearest neighbors, naive Bayes, SVM, neural network, serta ensemble voting classifier, dengan pembagian data pelatihan dan pengujian 80:20. Evaluasi memakai metrik akurasi, cross validation score, dan ROC_AUC. Hasil menunjukkan voting classifier yang menggabungkan logistic regression dan SVM memberi kinerja terbaik (akurasi 88,04%, cross validation 91,01%, ROC_AUC 88,00%). Analisis menegaskan tekanan darah dan kadar kolesterol sebagai prediktor penting untuk gagal jantung.","Integrating Multiple Machine Learning Models to Predict Heart Failure Risk  \nIntegrasi Model Multiple Machine Learning untuk Memprediksi Resiko Gagal Jantung  \nTuahta Hasiholan Pinem1, Yan Rianto2  \n1,2 Ilmu Komputer, Universitas Nusa Mandiri Margonda, Indonesia 1*[14220020@nusamandiri.ac.id](14220020@nusamandiri.ac.id), [2](2 yan.yrt@nusamandiri.ac.id)[ yan.yrt@nusamandiri.ac.id](2 yan.yrt@nusamandiri.ac.id)  \nInformasi Artikel  \nReceived: December 2023  \nRevised: January 2024  \nAccepted: January 2024  \nPublished: June 2024  \nKeywords: Machine Learning Models; Voting Classifier Algorithm; Feature Binning  \nKata kunci: Model Machine Learning, Algoritma Voting Classifier, Pengelompokan Fitur  \nAbstract  \nThe research aims to create and evaluate machine learning models for the prognosis of heart failure based on patient medical information. Various predictive models have been created employing algorithms like logistic regression, decision trees, random forests, K-nearest neighbors, naive Bayes, support vector machines (SVMs), neural networks, and ensemble voting classifiers. The dataset utilized comprises diverse clinical characteristics from patients diagnosed with heart failure. The data underwent division into training and testing sets in an 80:20 ratio. Metrics including accuracy, Cross Validation Score, and ROC_AUC Score score were used to assess the models'performance. The findings reveal that the Voting Classifier, amalgamating the Logistic Regression and Support Vector Classifier models, demonstrated superior performance with an accuracy of 88.04%, a cross-validation score of 91.01%, and a ROC_AUC score of 88.00%. Further scrutiny suggested that blood pressure and cholesterol levels serve as substantial indicators of heart failure. This study presentsa notable advancement in the utilization of machine learning models for heart failure prediction by scrutinizing diverse algorithms and pinpointing the most pertinent clinical characteristics. These outcomes hint at the potential for the development of machine learning-driven clinical tools to facilitate early detection and enhance medical interventions.  \nAbstrak  \nPenelitian bertujuan mengembangkan dan mengevaluasi model machine learning untuk memprediksi gagal jantung berdasarkan data medis pasien telah dilakukan. Model prediksi dibangun menggunakan algoritma Logistic Regression, Decision Tree, Random Forest, K-Neares Neighbor, Naive Bayes, Support Vector Machine (SVM),  \n Neural Network dan Voting Classifier. Dataset yang   \ndigunakan mencakup berbagai fitur klinis dari pasien yang didiagnosis dengan gagal jantung. Data telah dibagi menjadiset pelatihan dan pengujian dengan rasio 80:20 . Evaluasi model menggunakan metrik akurasi, Cross Validation Score, dan ROC_AUC Score untuk menilai kinerja masingmasing model. Hasil menunjukkan bahwa model Voting Classifier yang menggabungkan model Logistic Regression dan Support Vector Classifier menghasilkan kinerja terbaikdengan nilai akurasi sebesar 88.04%, Cross Validation Score sebesar91.01%, dan ROC_AUC Score sebesar 88.00% . Analisis lebih lanjut mengindikasikan bahwa fitur tekanan darah dan kadar kolesterol adalah prediktor yang signifikan untuk gagal jantung. Penelitian ini memberikankontribusi signifikan dalam aplikasi model machine learning untuk memprediksi gagal jantung dengan mengevaluasi berbagai algoritma dan mengidentifikasi fitur klinis yang paling relevan. Hasil ini menunjukkan bahwa pengembangan alat klinis berbasis machine learning yang dapat mendukung deteksi dini dan intervensi medis yanglebih efektif.  \n1. Introduction  \nHeart failure (HF) poses a significant global health burden, with increasing prevalence associated with factors such as an aging population, improved survival rates from cardiovascular diseases, and lifestyle changes [1] . Frailty and high BMI notably heighten the risk of heart failure, with frail and prefrail individuals showing significantly higher risks, particularly when combined with obesi","cbCaiiYCqJNuDajv","https://ap.wps.com/l/cbCaiiYCqJNuDajv","pdf",696100,3,1,17,"Indonesian","id",113,"# Introduction\n## Kebutuhan prediksi risiko gagal jantung\n## Keterbatasan diagnosis tradisional\n## Perkembangan machine learning untuk prediksi","[{\"question\":\"Tujuan utama penelitian ini apa?\",\"answer\":\"Mengembangkan dan mengevaluasi model machine learning untuk memprediksi gagal jantung berdasarkan data medis pasien.\"},{\"question\":\"Metode evaluasi apa yang digunakan untuk menilai performa model?\",\"answer\":\"Model dinilai menggunakan metrik akurasi, Cross Validation Score, dan ROC_AUC Score.\"},{\"question\":\"Model mana yang memberikan kinerja terbaik dan berapa hasilnya?\",\"answer\":\"Voting Classifier yang menggabungkan Logistic Regression dan Support Vector Classifier menghasilkan kinerja terbaik dengan akurasi 88,04%, cross validation 91,01%, dan ROC_AUC 88,00%.\"}]","Integrasi Model Multiple Machine Learning untuk Memprediksi Resiko Gagal Jantung - 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