A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis
نویسندگان
چکیده
Patients with liver disease have been continuously increasing because of excessive consumption of alcohol, inhalation of harmful gases, intake of contaminated food, pickles and drugs. Automatic classification tools may reduce burden on doctors. This paper evaluates the selected classification algorithms for the classification of some liver patient datasets. Classification algorithms considered here are Naive Bayes classification (NBC), Bagging algorithm, Dagging algorithm, KStar algorithm, Logistic algorithm. These algorithms are evaluated based on four criteria: Accuracy, Precision, Sensitivity and Specificity. It was found that, KStar algorithm is best, because of high accuracy and low error. On the other hand, Naive Bayes had the minimum accuracy and maximum error.
منابع مشابه
Analysis of Pre-processing and Post-processing Methods and Using Data Mining to Diagnose Heart Diseases
Today, a great deal of data is generated in the medical field. Acquiring useful knowledge from this raw data requires data processing and detection of meaningful patterns and this objective can be achieved through data mining. Using data mining to diagnose and prognose heart diseases has become one of the areas of interest for researchers in recent years. In this study, the literature on the ap...
متن کاملAutomatic classification of Non-alcoholic fatty liver using texture features from ultrasound images
Background: Accurate and early detection of non-alcoholic fatty liver, which is a major cause of chronic diseases is very important and is vital to prevent the complications associated with this disease. Ultrasound of the liver is the most common and widely performed method of diagnosing fatty liver. However, due to the low quality of ultrasound images, the need for an automatic and intelligent...
متن کاملDiagnosis of Diabetes Using an Intelligent Approach Based on Bi-Level Dimensionality Reduction and Classification Algorithms
Objective: Diabetes is one of the most common metabolic diseases. Earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. Diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. Classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...
متن کاملEvaluation of Data Mining Algorithms for Detection of Liver Disease
Background and Aim: The liver, as one of the largest internal organs in the body, is responsible for many vital functions including purifying and purifying blood, regulating the body's hormones, preserving glucose, and the body. Therefore, disruptions in the functioning of these problems will sometimes be irreparable. Early prediction of these diseases will help their early and effective treatm...
متن کاملDiagnosis of Heart Disease Based on Meta Heuristic Algorithms and Clustering Methods
Data analysis in cardiovascular diseases is difficult due to large massive of information. All of features are not impressive in the final results. So it is very important to identify more effective features. In this study, the method of feature selection with binary cuckoo optimization algorithm is implemented to reduce property. According to the results, the most appropriate classification fo...
متن کامل