  For a medical diagnosis, health professionals use different kinds of
pathological ways to make a decision for medical reports in terms of patients
medical condition. In the modern era, because of the advantage of computers and
technologies, one can collect data and visualize many hidden outcomes from
them. Statistical machine learning algorithms based on specific problems can
assist one to make decisions. Machine learning data driven algorithms can be
used to validate existing methods and help researchers to suggest potential new
decisions. In this paper, multiple imputation by chained equations was applied
to deal with missing data, and Principal Component Analysis to reduce the
dimensionality. To reveal significant findings, data visualizations were
implemented. We presented and compared many binary classifier machine learning
algorithms (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], all
mentioned techniques were applied to find one better method to classify blood
donors and non-blood donors (hepatitis, fibrosis, and cirrhosis) that can help
health professionals in a laboratory to make better decisions. Our proposed
ML-method showed better accuracy score (e.g. 98.23% for SVM). Thus, it improved
the quality of classification.
