| Abstrakt: |
The liver is a vital organ of the digestive system which helps in blood clotting, preparation of bile, and performs many more functions. The change in lifestyle and eating habits like intake of junk, packaged food which contains a low nutrient value and high amount of sugar/salt, saturated fat impacts the healthy liver. Liver disease is identified as the 10th most common cause of mortality in India and around 10 lakh people are diagnosed with one or the other liver disease in India every year. Hence, there is a certain need to develop an automated system that can classify whether a person is suffering from liver disease or not based on his/her parameters. In this paper, we proposed a novel liver disease detection framework based on machine learning techniques. For this purpose, we used three different machine learning techniques namely the Ensemble technique, Artificial Neural Network (ANN), and CatBoost algorithm. The performance of the above three algorithms is analyzed and it can be concluded that maximum classification accuracy is achieved by the CatBoost algorithm. [ABSTRACT FROM AUTHOR] |
