Usage of Weka Software Based On Machine Learning Algorithms for Prediction of Liver Fibrosis/Cirrhosis
Abstract
The liver, a life-sustaining organ, plays a substantial role in many body functions. Liver diseases have become an important world health problem in terms of prevalence, incidences, and mortalities. Liver fibrosis/cirrhosis is great of importance, because if not treated in time liver cancer could be occurred and spread to other parts of the body. For this reason, early diagnosis of liver fibrosis/cirrhosis gives significance. Accordingly, this study investigated the performances of different machine learning algorithms for prediction of liver fibrosis/cirrhosis based on demographic and blood values. In this context, random forest, k nearest neighbour, C4.5 decision tree, K-star, random tree and reduced error pruning tree algorithms were used. Two distinct approaches were employed to evaluate the performances of machine learning algorithms. In the first approach, the entire features of dataset was utilized, while in the second approach, only the features selected through principal component analysis were used. Each approach was rigorously assessed using both 10-fold cross-validation and data splitting (70% train and 30% test) techniques. By conducting separate evaluations for each approach, a comprehensive understanding of the effectiveness of utilizing all features versus extracted features based principle component analysis was attained, providing valuable insights into the impact of feature dimensionality reduction on model performance. In this study, all analyses were implemented on WEKA data mining tool. In the first approach, the classification accuracies of random forest algorithm were 89.72% and 90.75% for applying data splitting (70%-30%) and cross-validation techniques, respectively. In the second approach, where feature reduction is performed using principle component technique, the accuracy values obtained from data splitting and cross-validation techniques of random forest algorithm were 88.61% and 88.83%, respectively. The obtained results revealed out that random forest algorithm outperformed for both approaches. Besides, the application of principal component analysis technique negatively affected the classification performance of used ML algorithms. It is thought that the proposed model will guide specialist physicians in making appropriate treatment decisions for patients with liver fibrosis/cirrhosis, potentially leading to death in its advanced stages.
Ethical Statement
Ethical Consideration This research is approved by Non-interventional Clinical Research Ethics Committee of Zonguldak Bulent Ecevit University (Decision no: 2021/05; Date: 10.03.2021).
References
- Acarlı K. 2020. Karaciğer sağlığını koruyan 10 hayati öneri. URL: https://www.memorial.com.tr/saglik-rehberi/karaciger-sagligini-koruyan-10-hayati-oneri (accessed date: August 28. 2023).
- Alaybeyoğlu A, Mulayim N. 2018. Karaciğer kanseri teşhisinde destek vektör makinesi tabanli uzman sistem tasarimi. Tıp Teknolojileri Kongresi, 8-10 Kasım, Gazi Magaso, KKTC, ss: 208-210.
Usage of Weka Software Based on Machine Learning Algorithms for Prediction of Liver Fibrosis/Cirrhosis
Abstract
References
- Acarlı K. 2020. Karaciğer sağlığını koruyan 10 hayati öneri. URL: https://www.memorial.com.tr/saglik-rehberi/karaciger-sagligini-koruyan-10-hayati-oneri (accessed date: August 28. 2023).
- Alaybeyoğlu A, Mulayim N. 2018. Karaciğer kanseri teşhisinde destek vektör makinesi tabanli uzman sistem tasarimi. Tıp Teknolojileri Kongresi, 8-10 Kasım, Gazi Magaso, KKTC, ss: 208-210.
Details
| Primary Language | English |
|---|---|
| Subjects | Biomedical Engineering (Other) |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | May 15, 2024 |
| Submission Date | August 29, 2023 |
| Acceptance Date | April 2, 2024 |
| Published in Issue | Year 2024 Volume: 7 Issue: 3 |
