Advertisement
Published in:
09-11-2022 | Original Paper
Data mining in predicting liver patients using classification model
Published in: Health and Technology | Issue 6/2022
Log inActivate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
Purpose
This study proposes to identify potential liver patients based on the results of a liver function test performed during a health screening to search for signs of liver disease. It is critical to detect a liver patient at an early stage in order to treat them effectively. A liver function test's level of specific enzymes and proteins in the blood is evaluated to determine if a patient has liver disease.
Methods
According to a review of the literature, general practitioners (GPs) rarely investigate any anomalies in liver function tests to the level indicated by national standards. The authors have used data pre-processing in this work. The collection has 30691 records with 11 attributes. The classification model is utilized to construct an effective prediction system to aid general practitioners in identifying a liver patient using data mining.
Results
The collected results indicate that both the Naïve Bayes and C4.5 Decision Tree models give accurate predictions. However, given the C4.5 model offers more accurate predictions than the Naïve Bayes model, it can be assumed that the C4.5 model is superior for this research. Consequently, the liver patient prediction system will be developed using the rules given by the C4.5 Decision Tree model in order to predict the patient class. The training set, suggested data mining with a classification model achieved 99.36% accuracy and on the testing set, 98.40% accuracy. On the training set, the enhanced accuracy relative to the current system was 29.5, while on the test set, it was 28.73. In compared to state-of-the-art models, the proposed approach yields satisfactory outcomes.
Conclusion
The proposed technique offers a variety of data visualization and user interface options, and this type of platform can be used as an early diagnosis tool for liver-related disorders in the healthcare sector. This study suggests a machine learning-based technique for predicting liver disease. The framework includes a user interface via which healthcare providers can enter patient information.