Prediction of Liver Diseases with Machine Learning Method

Year 2022, Volume: 5 Issue: 1, 115 - 122, 30.06.2022

Mustafa Teke

https://doi.org/10.51764/smutgd.1106793

Cited By: 1

Abstract

In this study, a disease prediction model study based on the logistic regression classification of liver test results from machine learning algorithms was performed. The liver works like a factory in the human body. Disease of this organ causes many effects that harm the whole body. In this study, the disease prediction model for this vital organ was performed according to certain criteria and parameters. In the study, values such as protein, albumin and bilirubin belonging to the liver were examined in the disease prediction model. The data model used in the study was taken from the open source kaggle website. The prediction model was implemented in the jupyter notebook environment with python language. Logistic regression model was preferred for categorical data estimation. The model created contained 84% accuracy. Confusion matrix calculation table was used as evaluation criterion and presented in the study.

Keywords

Machine learning, Logistic regression, Liver disease.

Makine Öğrenmesi Yöntemi ile Karaciğerde Oluşan Hastalıkların Tahmini

Abstract

Bu çalışmada karaciğer test sonuçlarının makine öğrenmesi algoritmalarından lojistik regresyon sınıflandırılmasına dayalı hastalık tahmin modeli çalışması yapılmıştır. Karaciğer insan vücudunda adeta bir fabrika gibi çalışmaktadır. Bu organın hastalanması bütün vücuda zarar veren birçok etki meydana getirmektedir. Bu çalışmada belirli ölçütlere ve parametrelere göre bu hayati organ için hastalık tahmin modeli gerçekleştirilmiştir. Çalışmada karaciğere ait protein, albümin ve bilurubin gibi değerler hastalık tahmin modelinde incelenmiştir. Çalışmada kullanılan veri modeli açık kaynaklı kaggle web sitesinden alınmıştır. Tahmin modeli python dili ile jupyter notebook ortamında gerçekleştirilmiştir. Kategorik veri tahmini içinse lojistik regresyon modeli tercih edilmiştir. Oluşturulan model %84 doğruluk içermiştir. Değerlendirme ölçütü olarak karmaşıklık matrisi kullanılmış ve çalışmada sunulmuştur.

Keywords

Lojistik regresyon, Karaciğer hastalığı., makine öğrenmesi

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