[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121993-en":3,"doc-seo-121993-105":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},121993,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",7,"Healthcare","Smartphone-Based Heart Disease Classification Using Machine Learning Techniques","Heart disease diagnosis delays and inaccurate assessments without medical expertise can be life-threatening. This study applies multiple machine learning algorithms to heart disease datasets to predict patient conditions, including Naive Bayes, k-nearest neighbor, Artificial Neural Network, Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, and XGBoost. Using Cleveland, Hungarian, Swiss, Long Beach, and Statlog datasets (1190 occurrences, 11 features) with an 80:20 train-test split, models are evaluated by recall, precision, accuracy, and F1 score. XGBoost achieves the highest accuracy at 93.7% and is integrated into an Android Studio mobile application for disease categorization.","International Journal of Informatics, Information Systems and Computer Engineering  \nSmartphone-Based Heart Disease Classification Using Machine Learning Techniques  \nSonam Wangmo*, Yonten Jamtsho  \nGyalpozhing College of Information Technology, Royal University of Bhutan, Bhutan  \n*Corresponding Email: [sonamwangmo.gcit@rub.edu.bt](sonamwangmo.gcit@rub.edu.bt)  \nA B S T R A C T S  \nHeart disease patients can occasionally endure considerable delays in diagnosis, and making inaccurate diagnoses without the assistance of a medical professional can be fatal. In order to solve this, the study offer applying a variety of machine learning algorithms to datasets related to heart disease so as to forecast the eventual condition.. These techniques include Naive Bayes (NB), k-nearest Neighbor, Artificial Neural Network (ANN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Decision Tree Classifier (DT), and XGBoost Classifier (XGB) . The Cleveland, Hungarian, Swiss, Long Beach, VA, and Statlog (Heart) datasets—a total of 1190 occurrences with 11 features—were used in the study. An 80:20 ratio was used to split these datasets into training and test sets. Metrics like recall, precision, accuracy, and F1 score were used to assess the model's performance. XGBoost Classifier outperformed the other eight models, with a 93.7% accuracy rate. The Android Studio framework was then used to integrate the trained model and create a mobile application for categorizing heart diseases.  \n© 2024 Tim Konferensi UNIKOM  \nA R T I C L E I N F O  \nArticle History:  \nReceived 10 Jan 2024  \nRevised 02 Mar 2024  \nAccepted 05 May 2024 Available online 08 Jul 2024 Publication Date 01 Dec 2024  \nKeywords:  \nHeart disease, Machine learning, Mobile application, Clinical diagnosis, Data-driven healthcare  \n1. INTRODUCTION  \nIn Bhutan, coronary heart disease was responsible for 799 deaths, or 19.00% of all deaths (WHO, 2018) . With an ageadjusted death rate of 147.58 per 100,000 people, Bhutan is ranked 66th in the world (World Life Expectancy, 2018) . Symptoms of heart disease include a fast heart rate, dizziness, difficulty breathing, and chest pain. Obesity, hypertension, and excessive cholesterol are among the causes. Specialized cardiologists are needed for the diagnosis of heart infections, which entails a rigorous process to decide the best course of action. In underdeveloped countries, patients with heart disease often face significant delays in diagnosis and must travel long distances for treatment, creating a substantial burden. Heart disease can be prevented with a correct prediction, but it can also be fatal if the prediction is inaccurate. Early diagnosis and taking necessary precautions such as regular exercise, eating healthy, and avoiding tobacco can help to prevent heart disease. Further, Diagnosis in the absence of medical personnel can be achieved with machine learning algorithms. According to data released by the Ministry of Information and Communications, mobile cellular subscribers in Bhutan increased by 24,558 during the second quarter of 2020.(Subba, 2020) . Smartphones are one of the most frequently used technologies in the modern world, and a variety of smartphone-based health applications help individuals. The development of mobile apps to predict heart disease will benefit doctors and medical staff. The objective of this task is to create a smartphone application that categorizes  \ncardiac diseases using machine learning algorithms. The major goal of this initiative is to grow a classifier for heart disease using many machine intelligence techniques and connect it with the Android application. To achieve the goals of the research, the following question needs to be addressed:  \nResearch Question 1: Which machine learning algorithms give high accuracy?  \nMotivation: The question is motivated by the goal of identifying the best machine learning algorithms that offer the best classification accuracy for heart illnes","cbCaicmNinpTsJSO","https://ap.wps.com/l/cbCaicmNinpTsJSO","pdf",821570,1,12,"English","en",105,"# Abstract\n# Article History\n# Keywords\n# 1. INTRODUCTION\n# 2. RELATED WORK","[{\"question\":\"Which machine learning algorithms are used to classify heart disease in this study?\",\"answer\":\"The study uses Naive Bayes, k-nearest neighbor, Artificial Neural Network, Logistic Regression, Random Forest, Support Vector Machine, Decision Tree Classifier, and XGBoost Classifier.\"},{\"question\":\"What datasets and evaluation setup are used to train and test the models?\",\"answer\":\"It uses the Cleveland, Hungarian, Swiss, Long Beach, VA, and Statlog (Heart) datasets with 1190 occurrences and 11 features, split using an 80:20 ratio for training and testing.\"},{\"question\":\"Which model performs best and what accuracy does it achieve?\",\"answer\":\"XGBoost Classifier outperforms the others with 93.7% accuracy, assessed using recall, precision, accuracy, and F1 score.\"}]","Smartphone-Based Heart Disease Classification Using Machine Learning Techniques | PDF",1785808191,30,{"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},"smartphone-based-heart-disease-classification-using-machine-learning-techniques","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/smartphone-based-heart-disease-classification-using-machine-learning-techniques/121993/",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},"Which machine learning algorithms are used to classify heart disease in this study?","Question",{"text":75,"@type":76},"The study uses Naive Bayes, k-nearest neighbor, Artificial Neural Network, Logistic Regression, Random Forest, Support Vector Machine, Decision Tree Classifier, and XGBoost Classifier.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What datasets and evaluation setup are used to train and test the models?",{"text":80,"@type":76},"It uses the Cleveland, Hungarian, Swiss, Long Beach, VA, and Statlog (Heart) datasets with 1190 occurrences and 11 features, split using an 80:20 ratio for training and testing.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best and what accuracy does it achieve?",{"text":84,"@type":76},"XGBoost Classifier outperforms the others with 93.7% accuracy, assessed using recall, precision, accuracy, and F1 score.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":29,"slug":121},8,"Research & Report","research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]