[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119662-en":3,"doc-seo-119662-105":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":20,"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},119662,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Comparison of Machine Learning Methods for Calories Burn Prediction","Machine learning regression models are used to predict calories burned during exercise to support healthier lifestyle decisions in the context of rising obesity and overweight. The study compares LightGBM, XGBoost, Random Forest, Ridge, Linear, Lasso, and Logistic approaches using an exercise dataset to evaluate predictive performance. Results indicate LightGBM achieves strong accuracy, reported as 1.27 mean absolute error, enabling reliable calorie-burn estimation. The proposed predictive system can power fitness recommender functionality and help users pursue tailored fitness goals.","Journal of Informatics and Web Engineering  \nVol. 3 No. 1 (February 2024) eISSN: 2821-370X  \nComparison of Machine Learning Methods for  \nCalories Burn Prediction  \nAlfred Tan Jing Sheng1, Zarina Che Embi2* and Noramiza Hashim3  \n1,2,3Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia.  \n*Corresponding author: ([zarina.embi@mmu.edu.my](zarina.embi@mmu.edu.my), ORCiD: 0000-0001-9378-7380)  \nAbstract-This paper focuses on the prediction of calories burned during exercise using machine learning techniques. Due to a growing number of obesity and overweight people, a healthy lifestyle must be adopted and maintained. This study explores and compares several machine learning regression models namely LightGBM, XGBoost, Random Forest, Ridge, Linear, Lasso, and Logistic to assess their calories burned prediction performance that can be used in systems such as fitness recommender systems supporting a healthy lifestyle. Our findings show that the LightGBM for predicting calorie burn has a good accuracy of 1.27 mean absolute error, giving users reliable recommendations. The proposed system has a good potential in assisting users in reaching their fitness objectives by offering precise and tailored advice.  \nKeywords—Exercise, Machine Learning, Prediction, Calories Burn, Recommender System  \nReceived: 01 September 2023; Accepted: 23 September 2023; Published: 16 February 2024  \nI. INTRODUCTION  \nOver the last few years, technology has improved the convenience and connectivity of our lives, but it has also had a negative impact on our health and wellbeing. Increased rates of obesity, heart disease, and other chronic health issues are a result of long workdays, sedentary lives, and a lack of physical activity. These disorders can have a substantial negative effect on general health and quality of life, as well as raise the price of healthcare. According to [1], the environment and lifestyle factors, like physical activity and eating habits, are also thought to be of vital relevance, even though genetics play a significant part in obesity. In addition, people who are less active may experience muscle deterioration and slowed metabolism, which makes it more difficult for them to maintain a healthy Body Mass Index (BMI) . In an article by [2], there are ways to reduce overweight and obesity such as engaging in regular physical activity for 150 minutes a week for adults. Therefore, exercise is very important to everyone, especially for those who are obese and overweight.  \nAccording to the World Health Organization (WHO) [3], Malaysia has the highest rate of obesity and overweight among Asian countries with 64% of men and 65% of women being fat or overweight. As stated in [4], overweight and obesity cases are rising throughout the nation at an alarming rate right now. Exercise is an essential part of a healthy lifestyle and to have a good BMI. Exercise increases energy expenditure, which directly affects the number of calories burned by the body. Regular exercise can help balance calorie intake, regulate weight, and enhance general health.  \nHowever, due to a lack of information, motivation and support, many people with obesity find it difficult to start and stick with an exercise programme.  \nKnowing how many calories are burned while exercising might help people make more informed diet and exercise choices. Calories burned can be estimated by a number of methods such as metabolic equations [5], heart rate [5], wearable fitness trackers [6], and advanced sensors [7] by taking into account characteristics such as age, gender, weight, height, activity type, heart rate, exercise duration and intensity. Prediction of calories burned can be made using machine learning predictive models.  \nIn this paper, we propose the use of LightGBM for predicting the amount of calories burned during exercises. It is compared to several machine learning algorithms namely XGBoost Regression, Random F","cbCainNUmizU4xvx","https://ap.wps.com/l/cbCainNUmizU4xvx","pdf",721108,1,10,"English","en",105,"# Introduction\n## Background and motivation\n## Motivation for calorie estimation\n# Literature Review\n## Calories burned prediction models\n## XGBoost regression","[{\"question\":\"Which machine learning models are compared for calories burn prediction?\",\"answer\":\"The study compares LightGBM, XGBoost, Random Forest, Ridge, Linear, Lasso, and Logistic models for predicting calories burned during exercise.\"},{\"question\":\"What metric is used to report prediction performance, and what is the best result?\",\"answer\":\"Mean absolute error is used; LightGBM is reported to achieve 1.27 mean absolute error, indicating good accuracy.\"},{\"question\":\"How can the predicted calories burned be used in practical systems?\",\"answer\":\"Accurate predictions can support fitness recommender systems by providing users reliable and tailored recommendations to help reach health and fitness objectives.\"}]","Comparison of Machine Learning Methods for Calories Burn Prediction | PDF",1785725546,25,{"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},"comparison-of-machine-learning-methods-for-calories-burn-prediction","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/comparison-of-machine-learning-methods-for-calories-burn-prediction/119662/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are compared for calories burn prediction?","Question",{"text":75,"@type":76},"The study compares LightGBM, XGBoost, Random Forest, Ridge, Linear, Lasso, and Logistic models for predicting calories burned during exercise.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What metric is used to report prediction performance, and what is the best result?",{"text":80,"@type":76},"Mean absolute error is used; LightGBM is reported to achieve 1.27 mean absolute error, indicating good accuracy.",{"name":82,"@type":73,"acceptedAnswer":83},"How can the predicted calories burned be used in practical systems?",{"text":84,"@type":76},"Accurate predictions can support fitness recommender systems by providing users reliable and tailored recommendations to help reach health and fitness objectives.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]