Implementation of Modified K-Nearest Neighbor for Diagnosis of Liver Patients

Alwis Nazir, Elvianti Suwanto Sanjaya, Fadhilla Syafria

Sari


Number of patients with liver disease in the world is very high. In the early stages, liver disease is difficult to detect. Early diagnosis of the liver disease may help in preventing and treating sufferers. To diagnose liver disease can be done with a blood test.  Based on data from this analysis, the results can assist in determining patients with liver disease. This study uses data Indian Liver Patient Dataset (ILPD) taken from the UCI Machine Learning Repository. We used Modified k-Nearest Neighbor to classify into two classes, namely sufferers and non-sufferers. The amounts of data used in this study were 583 records. Tests performed by dividing the training data and test data to 50:50, 60:40, 70:30 and 80:20. Results of tests performed can classify with a good degree of accuracy reached 85.14% with a ratio of 70:30 and k = 3.


Teks Lengkap:

12-17

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