Radial Basis Function Model for Obesity Classification Based on Lifestyle and Physical Condition
Obesity is a chronic condition affecting millions worldwide, influenced by genetic predispositions, environmental factors, lifestyle habits, and excessive caloric intake surpassing energy expenditure. widespread prevalence, existing studies lack a comprehensive exploration of classification models t...
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| Format: | Article |
| Language: | English |
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P3M Politeknik Negeri Banjarmasin
2024-12-01
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| Series: | Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer |
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| Online Access: | https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1347 |
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| _version_ | 1846106884246863872 |
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| author | Farhan Radhiansyah Razak Muhammad Kunta Biddinika Herman Yuliansyah |
| author_facet | Farhan Radhiansyah Razak Muhammad Kunta Biddinika Herman Yuliansyah |
| author_sort | Farhan Radhiansyah Razak |
| collection | DOAJ |
| description | Obesity is a chronic condition affecting millions worldwide, influenced by genetic predispositions, environmental factors, lifestyle habits, and excessive caloric intake surpassing energy expenditure. widespread prevalence, existing studies lack a comprehensive exploration of classification models that effectively address the complex interplay between lifestyle and physical attributes. This study tackles the absence of an optimal machine learning model for accurately classifying obesity based on these multifaceted factors. To address this gap, the study evaluates the performance of three machine learning algorithms: Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel, Naïve Bayes, and K-Nearest Neighbor (KNN). The primary objectives are to identify the most accurate classification approach, analyze the strengths of these algorithms, and highlight the importance of lifestyle and physical attributes in obesity prediction. Experimental findings show that SVM with RBF kernel achieves the highest accuracy at 89%, surpassing the performance of the other models. This study advances the field of obesity classification by offering a detailed comparative analysis of machine learning algorithms and underscoring the critical role of integrating lifestyle and physical factors into predictive modeling. |
| format | Article |
| id | doaj-art-dac927654ff3421caa9b66c6f2708640 |
| institution | Kabale University |
| issn | 2598-3245 2598-3288 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | P3M Politeknik Negeri Banjarmasin |
| record_format | Article |
| series | Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer |
| spelling | doaj-art-dac927654ff3421caa9b66c6f27086402024-12-27T05:22:24ZengP3M Politeknik Negeri BanjarmasinJurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer2598-32452598-32882024-12-018219220010.31961/eltikom.v8i2.13471303Radial Basis Function Model for Obesity Classification Based on Lifestyle and Physical ConditionFarhan Radhiansyah Razak0Muhammad Kunta Biddinika1Herman Yuliansyah2Universitas Ahmad Dahlan, IndonesiaUniversitas Ahmad Dahlan, IndonesiaUniversitas Ahmad Dahlan, IndonesiaObesity is a chronic condition affecting millions worldwide, influenced by genetic predispositions, environmental factors, lifestyle habits, and excessive caloric intake surpassing energy expenditure. widespread prevalence, existing studies lack a comprehensive exploration of classification models that effectively address the complex interplay between lifestyle and physical attributes. This study tackles the absence of an optimal machine learning model for accurately classifying obesity based on these multifaceted factors. To address this gap, the study evaluates the performance of three machine learning algorithms: Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel, Naïve Bayes, and K-Nearest Neighbor (KNN). The primary objectives are to identify the most accurate classification approach, analyze the strengths of these algorithms, and highlight the importance of lifestyle and physical attributes in obesity prediction. Experimental findings show that SVM with RBF kernel achieves the highest accuracy at 89%, surpassing the performance of the other models. This study advances the field of obesity classification by offering a detailed comparative analysis of machine learning algorithms and underscoring the critical role of integrating lifestyle and physical factors into predictive modeling.https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1347obesityclassificationsvmnaive bayesknn |
| spellingShingle | Farhan Radhiansyah Razak Muhammad Kunta Biddinika Herman Yuliansyah Radial Basis Function Model for Obesity Classification Based on Lifestyle and Physical Condition Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer obesity classification svm naive bayes knn |
| title | Radial Basis Function Model for Obesity Classification Based on Lifestyle and Physical Condition |
| title_full | Radial Basis Function Model for Obesity Classification Based on Lifestyle and Physical Condition |
| title_fullStr | Radial Basis Function Model for Obesity Classification Based on Lifestyle and Physical Condition |
| title_full_unstemmed | Radial Basis Function Model for Obesity Classification Based on Lifestyle and Physical Condition |
| title_short | Radial Basis Function Model for Obesity Classification Based on Lifestyle and Physical Condition |
| title_sort | radial basis function model for obesity classification based on lifestyle and physical condition |
| topic | obesity classification svm naive bayes knn |
| url | https://eltikom.poliban.ac.id/index.php/eltikom/article/view/1347 |
| work_keys_str_mv | AT farhanradhiansyahrazak radialbasisfunctionmodelforobesityclassificationbasedonlifestyleandphysicalcondition AT muhammadkuntabiddinika radialbasisfunctionmodelforobesityclassificationbasedonlifestyleandphysicalcondition AT hermanyuliansyah radialbasisfunctionmodelforobesityclassificationbasedonlifestyleandphysicalcondition |