Detection and Comparative Analysis of Liver Disease Using Machine Learning Models

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M. Hemalatha, B. Chandrababu Naik, K.C.Kullayappa


In this paper, three different machine learning models are implemented on liver patient’s data. The data set is gathered from North-East of Andhra Pradesh, India. Human mortality and human morbidity are increased due to liver disease. Now a day, liver disease is increasing because of widespread intake of alcohol and also due to hepatitis. The main reason of liver disease is due to intake of drugs, harmful food, infections and toxic substances. The Liver damage is expected to play a vital role in inflammation, scarring, obstructions, cirrhosis, liver failure, and even liver cancer. The use of herbal medicines can be traced back several thousand years ago in ancient China. According to evidences many natural products are available as chemo protective agents against common liver diseases, such as hepatitis, cirrhosis, liver cancer, fatty liver diseases, and gallstones. This disease treatment is very costly and complicated. By considering all this facts, the work is carried out in this significant area. A novel machine leaning model has been introduced to detect liver disease. The Classification of liver disease data is done by using confusion matrix.

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