Pancreatic cancer classification using logistic regression and random forest

Zuherman Rustam, Fildzah Zhafarina, Glori Stephani Saragih, Sri Hartini


In the medical field, technology machinery is needed to solve several classification problems. Therefore, this research is useful to solve the problem of the medical field by using machine learning. This study discusses the classification of pancreatic cancer by using regression logistics and random forest. By comparing the accuracy, precision, recall (sensitivity), and F1-score of both methods, then we will know which method is better in classifying the pancreatic cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that random forest has better accuracy than logistic regressions. It can be seen with maximum accuracy of logistic regressions 96.48 with 30% data training and random forest 99.38% with 20% of data training.


Classification; Logistic regression; Machine learning; Pancreatic cancer; Random forest

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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