[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119441-en":3,"doc-seo-119441-105":29,"detail-sidebar-cat-0-en-105":89},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119441,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Heart Disease Prediction Using Machine Learning Techniques - HRFLM","Heart disease prediction addresses a critical clinical need for decision support and accurate risk identification. The study introduces a hybrid HRFLM framework that uses 10 clinical characteristics as inputs and trains an ANN with back propagation, then contrasts its outcomes with conventional techniques. Additional methods such as DT, NN, SVM, KNN, and voting strategies are considered, including a hybrid approach combining LR and NB. Experiments use the UCI dataset, employ feature-dimension reduction, and report heart disease prediction performance of 80.6% accuracy with PPCA-based assessment.","| دجلة الجامعة – جمهورية العراق | ا يعاااا ا بعااااب اااا. قليراااااىل يب اااالً ق ا يعاااا قل لااااة قليب اااال هقاااااالقاعكعاب قلهكاعاااا عي ااااؤ اؤ عا ااااي قل لااااة قليب اااال بلاااا ي بعاااال يااااؤ لداااا قلإدااااابباااايلقا قلا ااات قللااابعبف ااا ااا. قلهلكااا ق إااااالع إ كً اااا عااااارب ااإعااااب قلاااام قملااا لا بعاااب قلااااياب قلي يااا ق يياااا ع عاااب ياااؤ بكااا قلاإبااا باااايلقا قلا اااتهق ه ع قلبيهع\u003Cbr>الكلمات المفتاحي قلام قمل ق قلاإب بايلقا قلا تق قلل اع قلد ع | الجامعة – جمهورية العراق |\n| --- | --- | --- |\n\nIntroduction المقدمة  \nOne of the most common causes of death in the modern world is heart disease. One of the most important problems in clinical data analysis is the prediction of cardiovascular disease. It has been demonstrated that machine learning (ML) may effectively support decision-making and prediction-making from vast amounts of data. Which produced by the healthcare industry Recent advancements in several Internet of Things (IoT) domains have also demonstrated the application of machine learning (ML) techniques. Numerous studies just provide a glimpse of how ML approaches might be used to forecast cardiac disease. In order to increase the accuracy of cardiovascular disease prediction, we provide a unique approach in this study that uses machine learning approaches to identify important characteristics [1-2] .  \nيعددددض ادددد أ د أكددددس ابددددض ا شدددد ايددددالم د حدددددلم دددد ح ل ددددد د عددددل د ددددضي ادددد ا ددددد مشدددددد تح ددددددد ا ك ددددددة د ا ل ددددددلح د دددددد ي ي د اددددددؤ بدددددداا دأ د أكددددددس دة دددددد د ضاحيددددددقددددض أاددددع ا د دددد عك دلآ دددد )ML( قددددض يددددض بشدددد ة دعددددلا دااددددل د أدددد د د اددددؤ اددددم ددددددلح ل كدددددد ادددددد د ا ل ددددددلح د دددددد ا أظددددددل دددددد ل د ليدددددد د دددددد مددددددل ا ظدددددد حد طدددددح دح دةف ددددد م دددددد د عضيدددددض اددددد اأدددددل ح دددددع دة ددددد ل )IOT(اطا دددددي اأ دددددلحد ددد عك دلآ ددد . )ML( ادددحد د عضيدددض اددد د ض ديدددلح م ددد ددد ل ددد ديددد اضد ايدددل سد دددددد عك دلآ دددددد ك اددددددؤ بدددددداا دأ د أكددددددس ادددددد ا ددددددة يددددددل م قدددددد د اددددددؤ بدددددداا دأ د أكددددددسدة ددددد د ضاحيددددد ً أدددددض ظأدددددل د يدددددضد دددددد دددددي د ض ديددددد ي ددددد اض ايدددددل س د ددددد عك دلآ ددددد  \nضيض د ا ل ص د مظم [2-1]  \nResults and discussion النتائج والمناقشة  \nHRFLM uses 10 clinical characteristics as input and an ANN with back propagation. The acquired outcomes are contrasted with conventional techniques. The risk levels rise dramatically, and several characteristics are needed to accurately diagnose the illness. A successful treatment strategy is necessary due to the intricacy and nature of cardiac disease. The risk levels rise dramatically, and several characteristics are needed to accurately diagnose the illness. The characteristics and intricacy of cardiac disease need a successful treatment strategy. In the medical profession, data mining techniques are helpful in corrective circumstances. Additionally, DT, NN, SVM, and KNN aretaken into consideration while using the data mining techniques. Out of the several techniques used, SVM's output is helpful in improving illness prediction accuracy. To identify arrhythmias such as bradycardia, tachycardia, atrial, and ventricular flutters, among many others, the nonlinear approach with a module for heart function monitoring is introduced. The accuracy of the findings derived from ECG data may be used to measure the performance efficacy of this approach. Accurate illness heart disease via ANN training. This approach employs 10 clinical characteristic features as input, and the results of backpropagation training are very accurate in determining whether the patient has heart disease or not. In order to predict heart disease, a variety of data mining techniques and prediction algorithms, including KNN, LR, SVM, NN, and Vote, have gained popularity recently. This study suggests voting along with a hybrid strategy that combines LR and NB. The s","cbCaiihEFPaI1tzw","https://ap.wps.com/l/cbCaiihEFPaI1tzw","pdf",645812,1,"English","en",105,"# Introduction\n# Results and discussion\n## Methods and models\n## Evaluation and accuracy\n# Conclusion","[{\"question\":\"What is HRFLM in this study and what inputs does it use?\",\"answer\":\"HRFLM is the proposed hybrid technique. It uses 10 clinical characteristics as input and trains an ANN with back propagation to predict heart disease.\"},{\"question\":\"Which machine learning methods are compared or considered for prediction?\",\"answer\":\"The study contrasts HRFLM outcomes with conventional techniques and considers data mining methods including DT, NN, SVM, and KNN, along with prediction strategies such as KNN, LR, NN, and voting.\"},{\"question\":\"How is performance evaluated and what accuracy is reported?\",\"answer\":\"Experiments are conducted using the UCI dataset, and performance is assessed using PPCA. The reported accuracy for predicting heart disease reaches 80.6%.\"}]","Heart Disease Prediction Using Machine Learning Techniques - HRFLM | PDF",1785724298,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":27},"heart-disease-prediction-using-machine-learning-techniques-hrflm","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/healthcare/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/heart-disease-prediction-using-machine-learning-techniques-hrflm/119441/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":63,"interactionType":64,"userInteractionCount":20},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What is HRFLM in this study and what inputs does it use?","Question",{"text":73,"@type":74},"HRFLM is the proposed hybrid technique. It uses 10 clinical characteristics as input and trains an ANN with back propagation to predict heart disease.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Which machine learning methods are compared or considered for prediction?",{"text":78,"@type":74},"The study contrasts HRFLM outcomes with conventional techniques and considers data mining methods including DT, NN, SVM, and KNN, along with prediction strategies such as KNN, LR, NN, and voting.",{"name":80,"@type":71,"acceptedAnswer":81},"How is performance evaluated and what accuracy is reported?",{"text":82,"@type":74},"Experiments are conducted using the UCI dataset, and performance is assessed using PPCA. 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