CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES
Liver diseases pose a significant global health challenge due to their impact on metabolic function and the difficulty of early detection. Traditional diagnostic methods such as liver biopsy have limitations due to their invasive nature and high costs. This research examines the application of advan...
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| Format: | Article |
| Language: | English |
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Eskişehir Osmangazi University
2025-08-01
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| Series: | Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/4396614 |
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| _version_ | 1849228960783663104 |
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| author | Erol Özçekiç Ümit Yılmaz |
| author_facet | Erol Özçekiç Ümit Yılmaz |
| author_sort | Erol Özçekiç |
| collection | DOAJ |
| description | Liver diseases pose a significant global health challenge due to their impact on metabolic function and the difficulty of early detection. Traditional diagnostic methods such as liver biopsy have limitations due to their invasive nature and high costs. This research examines the application of advanced machine learning techniques such as Gradient Boosting, AdaBoost, XGBoost and CatBoost for classification of liver diseases using a publicly available dataset of 1700 clinical records. Statistical analyses identified key predictors such as age, body mass index (BMI), lifestyle factors, and liver function tests, which were used to train and evaluate the models. The performance of the models was evaluated using metrics such as accuracy, precision, recall and AUC-ROC. The CatBoost model showed the highest performance with an accuracy of 93.82%, while also producing the most consistent results with precision (91.97%), recall (96.62%), F1 score (94.25%) and AUC-ROC (95.64%). These results highlight the potential of machine learning-based approaches to improve diagnostic accuracy and reduce reliance on invasive procedures. The proposed framework can contribute to improving patient outcomes and optimizing healthcare resources by providing a foundation for real-time clinical decision support systems. |
| format | Article |
| id | doaj-art-c48ed8d723a44d84b9caee98ac34c758 |
| institution | Kabale University |
| issn | 2630-5712 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Eskişehir Osmangazi University |
| record_format | Article |
| series | Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi |
| spelling | doaj-art-c48ed8d723a44d84b9caee98ac34c7582025-08-22T11:23:57ZengEskişehir Osmangazi UniversityEskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi2630-57122025-08-013321882189210.31796/ogummf.1591951122CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHESErol Özçekiç0https://orcid.org/0000-0002-1896-6853Ümit Yılmaz1https://orcid.org/0000-0003-4268-8598BALIKESİR ÜNİVERSİTESİ, BALIKESİR MESLEK YÜKSEKOKULU, BİLGİSAYAR TEKNOLOJİLERİ BÖLÜMÜ, BİLGİSAYAR PROGRAMCILIĞI PR.BALIKESİR ÜNİVERSİTESİ, BİGADİÇ MESLEK YÜKSEKOKULU, YÖNETİM VE ORGANİZASYON BÖLÜMÜ, LOJİSTİK PR.Liver diseases pose a significant global health challenge due to their impact on metabolic function and the difficulty of early detection. Traditional diagnostic methods such as liver biopsy have limitations due to their invasive nature and high costs. This research examines the application of advanced machine learning techniques such as Gradient Boosting, AdaBoost, XGBoost and CatBoost for classification of liver diseases using a publicly available dataset of 1700 clinical records. Statistical analyses identified key predictors such as age, body mass index (BMI), lifestyle factors, and liver function tests, which were used to train and evaluate the models. The performance of the models was evaluated using metrics such as accuracy, precision, recall and AUC-ROC. The CatBoost model showed the highest performance with an accuracy of 93.82%, while also producing the most consistent results with precision (91.97%), recall (96.62%), F1 score (94.25%) and AUC-ROC (95.64%). These results highlight the potential of machine learning-based approaches to improve diagnostic accuracy and reduce reliance on invasive procedures. The proposed framework can contribute to improving patient outcomes and optimizing healthcare resources by providing a foundation for real-time clinical decision support systems.https://dergipark.org.tr/tr/download/article-file/4396614karaciğer hastalığımakine öğrenmesitanısınıflandırmaboostingliver diseasemachine learningdiagnosisclassificationboosting |
| spellingShingle | Erol Özçekiç Ümit Yılmaz CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi karaciğer hastalığı makine öğrenmesi tanı sınıflandırma boosting liver disease machine learning diagnosis classification boosting |
| title | CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES |
| title_full | CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES |
| title_fullStr | CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES |
| title_full_unstemmed | CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES |
| title_short | CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES |
| title_sort | classifying liver disease with boosting machine learning approaches |
| topic | karaciğer hastalığı makine öğrenmesi tanı sınıflandırma boosting liver disease machine learning diagnosis classification boosting |
| url | https://dergipark.org.tr/tr/download/article-file/4396614 |
| work_keys_str_mv | AT erolozcekic classifyingliverdiseasewithboostingmachinelearningapproaches AT umityılmaz classifyingliverdiseasewithboostingmachinelearningapproaches |