[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118885-en":3,"doc-seo-118885-105":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},118885,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Predicting Arrhythmia Based on Machine Learning - Using Improved Harris Hawk Algorithm","Arrhythmia is a serious cardiovascular condition whose early identification is vital to reducing preventable fatalities. This work builds a machine learning model for predicting heart disease using an Arrhythmia dataset, aiming for strong predictive performance. Model quality is addressed by mitigating overfitting from high-dimensional features through dimensionality reduction via an improved Harris hawk optimization algorithm (iHHO). The iHHO-selected feature set is then evaluated across multiple classifiers to compare accuracy and related effectiveness metrics, showing clear improvements over baseline approaches.","Predicting Arrhythmia Based on Machine Learning Using Improved Harris Hawk Algorithm  \nNitesh Sureja1, Nandini Chaudhari2, Rocky Upaghyay3, Shivam Upadhyay4, Safeya Dharmajwala5  \n1,4,5Deaprtment of Computer Science & Engineering  \nKrishna School of Emerging Technology & Applied Research  \nVadodara, INDIA  \n2,3Deaprtment ofInformatio Technology  \nKrishna School of Emerging Technology & Applied Research  \nVadodara, INDIA  \n*[nmsureja@gmail.com](nmsureja@gmail.com) (only corresponding Author)  \nAbstract—Arrhythmia disease is widely recognized as a prominent and lethal ailment on a global scale, resulting in a significant number of fatalities annually. The timely identification of this ailment is crucial for preserving individuals' lives. Machine Learning (ML), a branch of artificial intelligence (AI), has emerged as a highly efficient and cost-effective method for illness detection. The objective of this work is to develop a machine learning (ML) model capable of accurately predicting heart illness by using the Arrhythmia disease dataset, with the purpose of achieving optimal performance. The performance of the model is greatly influenced by the selection of the machine learning method and the features in the dataset for training purposes. In order to mitigate the issue of overfitting caused by the high dimensionality of the features in the Arrhythmia dataset, a reduction of the dataset to a lower dimensional subspace was performed via the improved Harris hawk optimization algorithm (iHHO) . The Harris hawk algorithm exhibits a rapid convergence rate and possesses a notable degree of adaptability in its ability to identify optimal characteristics. The performance of the models created with the feature-selected dataset using various machine learning techniques was evaluated and compared. In this work, total seven classifiers like SVM, GB, GNB, RF, LR, DT, and KNN are used to classify the data produced by the iHHO algorithm. The results clearly show the improvement of 3%, 4%, 4%, 9%, 8%, 3%, and 9% with the classifiers KNN, RF, GB, SVM, LR, DT, and GNB respectively.  \nKeywords-Harris Hawk Algorithm; Swarm Intelligence; Machine Learning; Levy Flight,;Arrhythmia.  \nI. INTRODUCTION  \nAll The simplicity, adaptability, and gradient-free mechanisms of swarm intelligence algorithms (SIAs) have drawn academics' attention in recent years [1] . With the aid of a fitness function, SIAs attempt to provide efficient and ideal solutions by eliminating ineffective ones.  \nThe following algorithms are commonly used in solving optimization problems: Dragonfly Algorithm (DFA), Whale Optimization Algorithm (WOA), Water Wave Optimization (WWA), Harris hawk algorithm (HHO), Moth Flame Optimization (MFO), Bacteria Foraging Optimization (BFO), Cuckoo Search (CS), Social Spider Optimization (SSPA), Bat Algorithm (BA), Naked Mole-Rat Algorithm (NMRA), Firefly Algorithm (FA), Salp Swarm Algorithm (SSA), Gray Wolf Optimizer (GWO), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Shuffle Frog Leaping Algorithm (SFLA), and Chicken Swarm Optimization (CSO) .  \nWe propose an improved Harris hawk algorithm (i-HHO) in this research to help various classifiers with classification problems by reducing features from the Arrhythmia dataset. The HHO is proposed in 2019 [2] . In order to examine the prey,  \nsurprise pounce, and other attack techniques of hawks in nature, HHO typically imitate the notions of Harris hawks. The literature claims that HHO outperformed other well-known metaheuristic algorithms for a number of benchmark tests [2] . HHO has the best qualities among its rivals when it comes to maintaining a steady balance between exploitation and exploration, which enables it to outshine in optimization jobs. HHO employs many search strategies in its exploitation to effectively impact local search outcomes. The HHO algorithm can be regarded as a robust technique for addressing optimization problems. The no free lun","cbCaibvF9xLsEwnu","https://ap.wps.com/l/cbCaibvF9xLsEwnu","pdf",446338,1,9,"English","en",105,"# Introduction\n## Swarm Intelligence and Optimization Background\n## Proposed Approach and Contributions\n# Literature Review","[{\"question\":\"Why is arrhythmia prediction important in this work?\",\"answer\":\"Arrhythmia is described as a prominent and lethal ailment, so timely identification is critical for saving lives and reducing annual fatalities.\"},{\"question\":\"How does the improved Harris hawk algorithm (iHHO) contribute to the model?\",\"answer\":\"iHHO performs feature selection and dimensionality reduction to reduce overfitting caused by high-dimensional features in the Arrhythmia dataset.\"},{\"question\":\"Which classifiers are evaluated in the study and how is performance reported?\",\"answer\":\"Seven classifiers—SVM, GB, GNB, RF, LR, DT, and KNN—classify the data produced by iHHO. The results report percentage improvements for each classifier.\"}]","Predicting Arrhythmia Based on Machine Learning - Using Improved Harris Hawk Algorithm | PDF",1785720785,23,{"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},"predicting-arrhythmia-based-on-machine-learning-using-improved-harris-hawk-algorithm","",{"@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/predicting-arrhythmia-based-on-machine-learning-using-improved-harris-hawk-algorithm/118885/",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","2026-08-03",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},"Why is arrhythmia prediction important in this work?","Question",{"text":75,"@type":76},"Arrhythmia is described as a prominent and lethal ailment, so timely identification is critical for saving lives and reducing annual fatalities.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the improved Harris hawk algorithm (iHHO) contribute to the model?",{"text":80,"@type":76},"iHHO performs feature selection and dimensionality reduction to reduce overfitting caused by high-dimensional features in the Arrhythmia dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"Which classifiers are evaluated in the study and how is performance reported?",{"text":84,"@type":76},"Seven classifiers—SVM, GB, GNB, RF, LR, DT, and KNN—classify the data produced by iHHO. The results report percentage improvements for each classifier.","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},[93,97,101,105,110,115,118,123,127,130,134],{"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":121,"slug":122},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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"]