A review of data mining techniques for diagnosing hepatitis

Recently, data mining techniques are widely used in the field of bioinformatics to analyze biomedical data. These techniques have given efficient results in the prediction and classification of diseases severity and diagnosis of sicknesses. Hepatitis is a liver inflammation; it can affect people of all ages. Millions of people globally are thought to be affected by hepatitis. Accurate and early diagnosis of hepatitis can save many patients. Hepatitis is a major challenge for public health care services, due to limited clinical diagnosis of hepatitis disease in its early stages. This paper presents an overview of the recent state-of-the-art data mining techniques used for diagnosing hepatitis and shows the performance of different techniques in term of accuracy and training time. Such review helps in the implementation, development and evaluation of efficient clinical decision support systems; where accurate diagnosis is the most important factor.