Usage of Weka Software Based On Machine Learning Algorithms for Prediction of Liver Fibrosis/Cirrhosis

Rukiye Uzun Arslan; Ziynet Pamuk; Ceren Kaya

Makale detayları

Usage of Weka Software Based On Machine Learning Algorithms for Prediction of Liver Fibrosis/Cirrhosis

Yazarlar:

Rukiye Uzun Arslan | Ziynet Pamuk | Ceren Kaya

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Biyomedikal Mühendisliği (Diğer)

Dergi:

Black Sea Journal of Engineering and Science Vol.7 Issue 3, pp.445-456

Belge Tipi:

Makale

Yayın Tarihi:

5/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001315135

pISSN:

2619-8991

Anahtar kelimeler:

Özet:

 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 fibrosiscirrhosis 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 fibrosiscirrhosis gives significance. Accordingly, this study investigated the performances of different machine learning algorithms for prediction of liver fibrosiscirrhosis 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 were 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 principal 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 with the application of data splitting 70-30 and cross-validation techniques, respectively. In the second approach, where feature reduction is performed using principal component analysis 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 machine learning algorithms. It is thought that the proposed model will guide specialist physicians in making appropriate treatment decisions for patients with liver fibrosiscirrhosis, potentially leading to death in its advanced stages.

 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 fibrosiscirrhosis 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 fibrosiscirrhosis gives significance. Accordingly, this study investigated the performances of different machine learning algorithms for prediction of liver fibrosiscirrhosis 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 were 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 principal 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 with the application of data splitting 70-30 and cross-validation techniques, respectively. In the second approach, where feature reduction is performed using principal component analysis 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 machine learning algorithms. It is thought that the proposed model will guide specialist physicians in making appropriate treatment decisions for patients with liver fibrosiscirrhosis, potentially leading to death in its advanced stages.

 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 fibrosiscirrhosis 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 fibrosiscirrhosis gives significance. Accordingly, this study investigated the performances of different machine learning algorithms for prediction of liver fibrosiscirrhosis 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 were 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 principal 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 with the application of data splitting 70-30 and cross-validation techniques, respectively. In the second approach, where feature reduction is performed using principal component analysis 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 machine learning algorithms. It is thought that the proposed model will guide specialist physicians in making appropriate treatment decisions for patients with liver fibrosiscirrhosis, potentially leading to death in its advanced stages.

 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 fibrosiscirrhosis 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 fibrosiscirrhosis gives significance. Accordingly, this study investigated the performances of different machine learning algorithms for prediction of liver fibrosiscirrhosis 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 were 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 principal 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 with the application of data splitting 70-30 and cross-validation techniques, respectively. In the second approach, where feature reduction is performed using principal component analysis 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 machine learning algorithms. It is thought that the proposed model will guide specialist physicians in making appropriate treatment decisions for patients with liver fibrosiscirrhosis, potentially leading to death in its advanced stages.

Makale detayları

Usage of Weka Software Based On Machine Learning Algorithms for Prediction of Liver Fibrosis/Cirrhosis

Yazarlar:

Rukiye Uzun Arslan | Ziynet Pamuk | Ceren Kaya

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Biyomedikal Mühendisliği (Diğer)

Dergi:

Black Sea Journal of Engineering and Science Vol.7 Issue 3, pp.445-456

Belge Tipi:

Makale

Yayın Tarihi:

5/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001315135

pISSN:

2619-8991

WEKA

Liver fibrosis/cirrhosis

Principal component analysis

Feature selection

Early diagnosis

Machine learningWEKA

Liver fibrosis/cirrhosis

Principal component analysis

Feature selection

Early diagnosis

Machine learning

Sayı

Makale detayları

Yazarlar: Rukiye Uzun Arslan | Ziynet Pamuk | Ceren Kaya

Yayıncı: Karyay Karadeniz Yayımcılık

Ana Başlıklar: Biyomedikal Mühendisliği (Diğer)

Dergi: Black Sea Journal of Engineering and Science Vol.7 Issue 3, pp.445-456

Belge Tipi: Makale

Yayın Tarihi: 5/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001315135

pISSN: 2619-8991

Sayı

Yazarlar:

Rukiye Uzun Arslan | Ziynet Pamuk | Ceren Kaya

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Biyomedikal Mühendisliği (Diğer)

Dergi:

Black Sea Journal of Engineering and Science Vol.7 Issue 3, pp.445-456

Belge Tipi:

Makale

Yayın Tarihi:

5/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001315135

pISSN:

2619-8991