[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120491-en":3,"doc-seo-120491-105":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},120491,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",7,"Healthcare","Enhancing Hypertension Prediction - A Hybrid Machine Learning Optimization Approach","Early identification of hypertension is essential to prevent severe complications that can harm lifestyle quality and substantially increase premature mortality. This study assesses machine learning methods for predicting hypertension using an imbalanced dataset of 4,363 records with 35 features. SMOTE is applied to balance classes and ant colony optimization selects relevant features. Model performance is boosted via hyperparameter optimization with Bayesian optimization and particle swarm optimization, where AdaBoost with Bayesian optimization achieves 97.60% accuracy, 98.93% recall, and 98.59% precision.","Indonesian Journal of Electrical Engineering and Computer Science  \nVol. 37, No. 1, January 2025, pp. 347~355  \nISSN: 2502-4752, DOI: 10. 11591/ijeecs.v37 . i1 .pp347-355 􀂈 347  \n\n| Enhancing hypertension prediction: a hybrid machine learning\u003Cbr>optimization approach\u003Cbr>Abd Allah Aouragh1, Mohamed Bahaj1, Fouad Toufik2\u003Cbr>1MIET Laboratory, Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco 2Computer Sciences Laboratory, Higher School of Technology, Mohammed V University, Sale, Morocco |  |\n| --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received May 7, 2024 Revised Sep 8, 2024 Accepted Sep 29, 2024\u003Cbr>Keywords:\u003Cbr>Ant colony optimization Bayesian optimization Feature selection Hyperparameter optimization Hypertension\u003Cbr>Machine learning\u003Cbr>Particle swarm optimization SMOTE\u003Cbr>Corresponding Author: | ABSTRACT\u003Cbr>Early identification of hypertension is crucial to prevent its serious complications, which can lead to devastating health effects by threatening lifestyle quality and significantly increasing premature mortality. This study aims to evaluate the effectiveness of machine learning techniques in predicting the presence of hypertension from an unbalanced dataset consisting of 4,363 records and 35 features. To balance the dataset, we employed the synthetic minority over-sampling technique (SMOTE) algorithm. In addition, to select the most relevant features, we used ant colony optimization. Next, we applied various algorithms, including logistic regression (LR), K-nearest neighbors (KNNs), support vector machine (SVM), extra trees (ETs), and AdaBoost (AB) . We also evaluated the optimization of hyperparameters using two methods: Bayesian optimization (BO) and particle swarm optimization (PSO) . The results reveal that the combination of AB with BO demonstrated superior performance, with an accuracy of 97.60%, a recall of 98.93%, and a precision of 98.59% . This research emphasizes the potential of machine learning techniques for anticipating hypertension and highlights the importance of optimization techniques in improving predictive models ’ performance.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| Abd Allah Aouragh\u003Cbr>MIET Laboratory, Faculty of Sciences and Techniques, Hassan 1st University Settat, Morocco\u003Cbr>Email: [abdallahaouragh@gmail.com](abdallahaouragh@gmail.com) |  |\n\n1. INTRODUCTION  \nHypertension, also known as high blood pressure, is a medical condition characterized by persistently high blood pressure that affects more than a billion people worldwide [1] . It represents a serious global health problem, which notably increases the risk of cardiovascular disease, stroke, and kidney disorders [1], [2] . Worldwide, around 10 million deaths are associated with hypertension every year, making it one of the foremost factors leading to death [1], [2] . The risks and consequences of hypertension include damage to vital organs, reduced quality of life, and an increased risk of premature death [2], [3] . In addition, hypertension is a prominent risk element for other serious health issues, such as kidney disease and diabetes [2], [3]. Available treatments cover antihypertensive drugs, lifestyle modifications, and regular monitoring [3] . However, challenges remain, including treatment non-adherence and the costs associated with long-term disease management [4] . Consequently, early recognition and intervention are crucial to mitigating the risk of serious complications and reducing the morbidity and mortality associated with hypertension, thus helping to alleviate the burden of the disease on healthcare systems [5] . Machine learning techniques provide a valuable tool in the healthcare field, offering the ability to analyze large medical datasets to predict diseases [6], [7] . Their use can help to identify risk factors, detect early warning signs of conditions such as hypertension, and  \ntailor interventions in a personalized way [8] . By integrating data balancing [9]","cbCais9nIvGquvvu","https://ap.wps.com/l/cbCais9nIvGquvvu","pdf",250169,1,9,"English","en",105,"# Abstract\n# Introduction\n## Background and challenges of hypertension\n## Role of machine learning and optimization\n# Materials and Methods\n## Dataset and imbalance handling\n## Feature selection and model training\n## Hyperparameter optimization\n# Results and Discussion\n## Performance comparison and best configuration\n# Conclusion","[{\"question\":\"Why is early hypertension prediction important?\",\"answer\":\"Early identification helps prevent serious complications, reduces risks to vital organs, improves quality of life, and lowers premature mortality and healthcare burden.\"},{\"question\":\"How was the imbalanced dataset handled in this study?\",\"answer\":\"The synthetic minority over-sampling technique (SMOTE) was used to balance the dataset before training prediction models.\"},{\"question\":\"Which approach produced the best predictive performance?\",\"answer\":\"The combination of AdaBoost with Bayesian optimization showed the strongest results, reaching 97.60% accuracy, 98.93% recall, and 98.59% precision.\"}]","Enhancing Hypertension Prediction - 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