English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

For a medical diagnosis, health professionals use different kinds ofpathological ways to make a decision for medical reports in terms of patientsmedical condition. In the modern era, because of the advantage of computers andtechnologies, one can collect data and visualize many hidden outcomes fromthem. Statistical machine learning algorithms based on specific problems canassist one to make decisions. Machine learning data driven algorithms can beused to validate existing methods and help researchers to suggest potential newdecisions. In this paper, multiple imputation by chained equations was appliedto deal with missing data, and Principal Component Analysis to reduce thedimensionality. To reveal significant findings, data visualizations wereimplemented. We presented and compared many binary classifier machine learningalgorithms (Artificial Neural Network, Random Forest, Support Vector Machine)which were used to classify blood donors and non-blood donors with hepatitis,fibrosis and cirrhosis diseases. From the data published in UCI-MLR [1], allmentioned techniques were applied to find one better method to classify blooddonors and non-blood donors (hepatitis, fibrosis, and cirrhosis) that can helphealth professionals in a laboratory to make better decisions. Our proposedML-method showed better accuracy score (e.g. 98.23% for SVM). Thus, it improvedthe quality of classification.

Machine Learning Approaches for Binary Classification to Discover Liver Diseases using Clinical Data