Classification of diabetic retinopathy disease from Video-Oculography (VOG) signals with feature selection based on C4.5 decision tree

Kaya, Ceren; Erkaymaz, Okan; Ayar, Orhan; Özer, Mahmut

doi:10.1109/tiptekno.2017.8238093

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Statistical features obtained from discrete wavelet transform and C4.5 decision tree were applied as inputs to artificial neural networks. It has been concluded that features selected by the C4.5 decision tree algorithm (96.87%) provide better classification performance than features extracted by the discrete wavelet transform (93.75%) (Kaya et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Classification of Type 2 Diabetes Using Machine Learning Techniques

Pamuk¹,

Kaya²

2021

European Journal of Science and Technology

Self Cite

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Classification of Type 2 Diabetes Using Machine Learning Techniques

Pamuk¹,

Kaya²

2021

European Journal of Science and Technology

Self Cite

View full text Add to dashboard Cite

“…Kaya and Ertuğrul (2016), proposed a new language recognition approach based on feature extraction. Kaya et al (2017), made a selection of features to classify diabetic retinopathy disease. Emhan and Akın (2019), investigated the effect of feature selection methods on intrusion detection systems.…”

Section: Introductionmentioning

confidence: 99%

Determination and Classification of Importance of Attributes Used in Diagnosing Pregnant Women's Birth Method

Çelik

2020

Alphanumeric Journal

View full text Add to dashboard Cite

The rapid development of information technologies enables successful results in computer-aided studies. This has led researchers to investigate the usability of technologies such as computer and software supported systems, machine learning, and artificial intelligence in many studies. One of these areas is health. For example, in order not to risk the condition of the mother and baby, in some cases, it is very important to correctly determine the times when the cesarean operation, which is mandatory, is mandatory. In this context, in order to make a faster and more accurate decision, it is very important to determine which attributes and how important the level is in making obligatory cesarean. In this study, to determine whether or not caesarean is necessary in the literature, the importance level of the five criteria taken into consideration has been determined and an attribute determination has been carried out and then a classification has been made. The data set used belongs to 80 pregnant women with 6 attributes. Although the same data set was previously classified with different methods, no study was found on determining the significance levels of the attributes and using artificial neural networks as a method. For this reason, in this study, the feature was determined using an adaptive nerve-fuzzy classifier and classified using artificial neural networks. When the results are examined, it is concluded that the importance levels of the attributes are different. Although the values such as accuracy, Sensitivity, and Specificity calculated to evaluate the classification results were found to be quite high for the training set, it was observed that the desired success was not achieved in the test data. While this result is promising, it also reveals the need to increase the learning performed with larger data sets.

show abstract

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

Uzun Arslan,

Pamuk,

Kaya

2024

Black Sea Journal of Engineering and Science

View full text Add to dashboard Cite

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 and C4.5 decision tree algorithms were used and these algorithms were implemented on WEKA data mining tool. The obtained results revealed out that random forest algorithm outperformed in term of all evaluation metrics (90.91% accuracy, 90% specificity, 90% precision, 91.8% recall, 0.909 F-measure and 0.962 ROC) as compared with other algorithms.

show abstract

Classification of diabetic retinopathy disease from Video-Oculography (VOG) signals with feature selection based on C4.5 decision tree

Cited by 3 publications

References 10 publications

Classification of Type 2 Diabetes Using Machine Learning Techniques

Classification of Type 2 Diabetes Using Machine Learning Techniques

Determination and Classification of Importance of Attributes Used in Diagnosing Pregnant Women's Birth Method

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

Contact Info

Product

Resources

About