Machine learning methods are widely used algorithms in medical  field for accurate assessment of patient health data. For the last decade, the number of patients suffering from liver diseases is on increasing trend. General causes of liver disease include extreme consumption of alcohol, eating of impure food, inhale of harmful gases, and drugs. This study presents the classification of liver disorders using various machine leaning algorithms which include support vector machine (SVM), linear discriminant analysis (LDA), diagonal linear discriminant analysis (DLDA), quadratic discriminant analysis (QDA), diagonal quadratic discriminant analysis (DQDA), and Mahalanobis. The proposed model classifies liver diseases as alcoholic liver damage (ALD), primary hepatoma (PH), liver cirrhosis (LC), and cholelithiasis (C). The performance of presented algorithm is compared in terms of accuracy, sensitivity and specificity.

classification, support vector machine, linear discriminant analysis, diagonal linear discriminant analysis, quadratic discriminant analysis, diagonal quadratic discriminant analysis, Mahalanobis.