Machine Learning Approaches for Inferring Liver Diseases and Detecting Blood Donors from Medical Diagnosis

• URL: http://arxiv.org/abs/2104.12055v1
• Date: Sun, 25 Apr 2021 04:10:19 GMT
• Title: Machine Learning Approaches for Inferring Liver Diseases and Detecting Blood Donors from Medical Diagnosis
• Authors: Fahad B. Mostafa and Md Easin Hasan
• Abstract summary: In this paper, multiple imputation by chained equations was applied to deal with missing data. To reveal significant findings, data visualizations were implemented. Our proposed ML-method showed better accuracy score (e.g. 98.23% for SVM)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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