{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:18:17Z","timestamp":1771701497274,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,17]]},"DOI":"10.1145\/3723178.3723240","type":"proceedings-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T07:16:47Z","timestamp":1749194207000},"page":"466-473","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting Heart Disease Risk with Lifestyle Factors: Uncovering Vital Predictors via Feature Selection and Machine Learning Techniques"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3010-3203","authenticated-orcid":false,"given":"Sadia","family":"Tuly","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh Army International University of Science and Technology, Cumilla, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0439-7005","authenticated-orcid":false,"given":"Shatabdi","family":"Majumder","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh Army International University of Science &amp; Technology, Cumilla, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0088-3045","authenticated-orcid":false,"given":"Atoshi","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh Army International University of Science &amp; Technology, Cumilla, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7088-1556","authenticated-orcid":false,"given":"Ahosan","family":"Habib","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh Army International University of Science &amp; Technology, Cumilla, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7935-3698","authenticated-orcid":false,"given":"Sayma Alam","family":"Suha","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh University of Professionals, Dhaka, Bangladesh"}]}],"member":"320","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Natalie Arnold and Wolfgang Koenig. 2021. Polygenic risk score: clinically useful tool for prediction of cardiovascular disease and benefit from lipid-lowering therapy? Cardiovascular Drugs and Therapy 35 3 (2021) 627\u2013635.","DOI":"10.1007\/s10557-020-07105-7"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Ozan Dikilitas Daniel\u00a0J Schaid Matthew\u00a0L Kosel Robert\u00a0J Carroll Christopher\u00a0G Chute Joshua\u00a0C Denny Alex Fedotov QiPing Feng Hakon Hakonarson Gail\u00a0P Jarvik et\u00a0al. 2020. Predictive utility of polygenic risk scores for coronary heart disease in three major racial and ethnic groups. The American Journal of Human Genetics 106 5 (2020) 707\u2013716.","DOI":"10.1016\/j.ajhg.2020.04.002"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Elias Dritsas Sotiris Alexiou and Konstantinos Moustakas. 2022. Cardiovascular Disease Risk Prediction with Supervised Machine Learning Techniques. ICT4AWE 1 (2022) 315\u2013321.","DOI":"10.5220\/0011088300003188"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Elias Dritsas and Maria Trigka. 2023. Efficient data-driven machine learning models for cardiovascular diseases risk prediction. Sensors 23 3 (2023) 1161.","DOI":"10.3390\/s23031161"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Zhenzhen Du Yujie Yang Jing Zheng Qi Li Denan Lin Ye Li Jianping Fan Wen Cheng Xie-Hui Chen Yunpeng Cai et\u00a0al. 2020. Accurate prediction of coronary heart disease for patients with hypertension from electronic health records with big data and machine-learning methods: model development and performance evaluation. JMIR medical informatics 8 7 (2020) e17257.","DOI":"10.2196\/17257"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Luis\u00a0Rolando Guarneros-Nolasco Nancy\u00a0Aracely Cruz-Ramos Giner Alor-Hern\u00e1ndez Lisbeth Rodr\u00edguez-Mazahua and Jos\u00e9\u00a0Luis S\u00e1nchez-Cervantes. 2021. Identifying the main risk factors for cardiovascular diseases prediction using machine learning algorithms. Mathematics 9 20 (2021) 2537.","DOI":"10.3390\/math9202537"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Xi He B\u00a0Rajeswari Matam Srikanth Bellary Goutam Ghosh and Amit\u00a0K Chattopadhyay. 2020. CHD risk minimization through lifestyle control: machine learning gateway. Scientific reports 10 1 (2020) 4090.","DOI":"10.1038\/s41598-020-60786-w"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Weiting Huang Tan\u00a0Wei Ying Woon Loong\u00a0Calvin Chin Lohendran Baskaran Ong Eng\u00a0Hock Marcus Khung\u00a0Keong Yeo and Ng\u00a0See Kiong. 2022. Application of ensemble machine learning algorithms on lifestyle factors and wearables for cardiovascular risk prediction. Scientific Reports 12 1 (2022) 1033.","DOI":"10.1038\/s41598-021-04649-y"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCIT57492.2022.10054998"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/1022\/1\/012072"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Ryan Marcus Jeremy\u00a0M Lupague Romie\u00a0C Mabborang Alvin\u00a0G Bansil Melinda\u00a0M Lupague et\u00a0al. 2023. Integrated Machine Learning Model for Comprehensive Heart Disease Risk Assessment Based on Multi-Dimensional Health Factors. European Journal of Computer Science and Information Technology 11 3 (2023) 44\u201358.","DOI":"10.37745\/ejcsit.2013\/vol11n34458"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Meghana Padmanabhan Pengyu Yuan Govind Chada and Hien\u00a0Van Nguyen. 2019. Physician-friendly machine learning: A case study with cardiovascular disease risk prediction. Journal of clinical medicine 8 7 (2019) 1050.","DOI":"10.3390\/jcm8071050"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Karna Vishnu\u00a0Vardhana Reddy Irraivan Elamvazuthi Azrina\u00a0Abd Aziz Sivajothi Paramasivam Hui\u00a0Na Chua and S Pranavanand. 2021. Heart disease risk prediction using machine learning classifiers with attribute evaluators. Applied Sciences 11 18 (2021) 8352.","DOI":"10.3390\/app11188352"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/R10-HTC57504.2023.10461808"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"Sayma\u00a0Alam Suha and Muhammad\u00a0Nazrul Islam. 2022. An extended machine learning technique for polycystic ovary syndrome detection using ovary ultrasound image. Scientific Reports 12 1 (2022) 17123.","DOI":"10.1038\/s41598-022-21724-0"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Sayma\u00a0Alam Suha and Muhammad\u00a0Nazrul Islam. 2023. Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique. Heliyon 9 3 (2023).","DOI":"10.1016\/j.heliyon.2023.e14518"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TENSYMP54529.2022.9864447"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"crossref","unstructured":"Jasjit\u00a0S Suri Mrinalini Bhagawati Sudip Paul Athanasios Protogeron Petros\u00a0P Sfikakis George\u00a0D Kitas Narendra\u00a0N Khanna Zoltan Ruzsa Aditya\u00a0M Sharma Sanjay Saxena et\u00a0al. 2022. Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review. Computers in biology and medicine 142 (2022) 105204.","DOI":"10.1016\/j.compbiomed.2021.105204"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Maria Trigka and Elias Dritsas. 2023. Long-term coronary artery disease risk prediction with machine learning models. Sensors 23 3 (2023) 1193.","DOI":"10.3390\/s23031193"},{"key":"e_1_3_3_1_21_2","unstructured":"Mengyao Wang Shiu Lun\u00a0Au Yeung Shan Luo and Youngwon Kim. 2021. Associations of genetic risk and adherence to a healthy lifestyle with incidence of Stroke and Coronary Heart Disease in individuals with Hypertension: The UK Biobank Study: Oral Presentation C10. 5. The Health & Fitness Journal of Canada 14 3 (2021)."},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Andrew Ward Ashish Sarraju Sukyung Chung Jiang Li Robert Harrington Paul Heidenreich Latha Palaniappan David Scheinker and Fatima Rodriguez. 2020. Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population. NPJ digital medicine 3 1 (2020) 125.","DOI":"10.1038\/s41746-020-00331-1"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"crossref","unstructured":"Yongxin Yang Yaping Zhang Ming Ren Yonglan Wang Zhuoma Cairang Rongxiang Lin Haixia Sun and Jianju Liu. 2020. Association of cytochrome P450 2C19 polymorphisms with coronary heart disease risk: A protocol for systematic review and meta analysis. Medicine 99 50 (2020) e23652.","DOI":"10.1097\/MD.0000000000023652"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"crossref","unstructured":"Yixuan Ye Xi Chen James Han Wei Jiang Pradeep Natarajan and Hongyu Zhao. 2021. Interactions between enhanced polygenic risk scores and lifestyle for cardiovascular disease diabetes and lipid levels. Circulation: Genomic and Precision Medicine 14 1 (2021) e003128.","DOI":"10.1161\/CIRCGEN.120.003128"},{"key":"e_1_3_3_1_25_2","first-page":"1","volume-title":"Applied Informatics","author":"Zheng Jie","year":"2015","unstructured":"Jie Zheng, Dabeeru\u00a0C Rao, and Gang Shi. 2015. An update on genome-wide association studies of hypertension. In Applied Informatics , Vol.\u00a02. Springer, 1\u201320."}],"event":{"name":"ICCA 2024: 3rd International Conference on Computing Advancements","location":"Dhaka Bangladesh","acronym":"ICCA 2024"},"container-title":["Proceedings of the 3rd International Conference on Computing Advancements"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3723178.3723240","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3723178.3723240","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:56:47Z","timestamp":1750298207000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3723178.3723240"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,17]]},"references-count":24,"alternative-id":["10.1145\/3723178.3723240","10.1145\/3723178"],"URL":"https:\/\/doi.org\/10.1145\/3723178.3723240","relation":{},"subject":[],"published":{"date-parts":[[2024,10,17]]},"assertion":[{"value":"2025-06-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}