Novel Classification based approaches over Cancer Diseases

Abstract

Abstract:One of the crucial applications of data mining is medical data mining (MDM) that diagnoses almost all medical syndromes. MDM incorporates with early predictions, existence and depth of any disease. In fact, MDM supplements the partial assistance of physicians. Though there were many works carried out in MDM, liver cancer is still considered to be a life threatening and has lowest survivability. Predicting the existence of liver cancer at the early stage is highly challengeable for the doctors. The goal of this review is to study the related works on cancer especially liver cancer, an experiment was carried out with the help of dataset to check out the performance of seven classification algorithms with the respect to time and accuracy. This paper also discussed some research key factors that could help the research fraternity to be focused on.

Machine learning used in detection & diagnosis of human diseases

“…As displayed, among every one of the models, we acquired the most noteworthy precision for the Weighted KNN model of 94.2 %. The precision is high in light of the fact that the weighted KNN was high since in this model the worth of K changed [9]. This worth changed as indicated by our dataset for example it was little and enormous for the preparation set.…”

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

“…Vijayarani and Dhayanand [129] classified between liver diseases, such as cirrhosis, bile duct, chronic hepatitis, liver cancer, and acute hepatitis from liver function test dataset while using this algorithm. Thangaraju and Mehala [130] predicted lung cancer at an early stage by using generic lung cancer symptoms, such as age, sex, wheezing, shortness of breath, and pain in shoulder, chest, and arm. Vijayarani and Dhayanand [131] predicted kidney disease based on blood and urine tests as well as removing a sample of kidney tissue for testing.…”

“…Metabolic diseases [74,79] Clustering K-means Clustering [87] Clustering DBSCAN [171] Regression Random Forest [100] Classification SVM [106,109] Classification ID3 [115,116,118,120,122] Classification KNN [135] Classification Naïve Bayes [137,143] Classification Bayesian Networks [145] Regression Linear regression Cancer [75,81] Clustering K-means Clustering [84,86] Clustering DBSCAN [24] Clustering SNF [25] Clustering PINS [26] Clustering CIMLR [95,172] Classification SVM [108] Classification ID3 [130] Classification Naïve Bayes [136] Classification Bayesian Networks [148,173] Regression Linear regression [146,174] Regression Logistic regression [157] Classification Neural Networks + KNN [156] Classification Neural Networks + SVM [160] Classification Neural Networks + ID3 [161] Classification KNN [175] Classification DT [176] Classification DL…”