Naïve Bayes Classifier Models for Predicting the Colon Cancer

Abstract

Cancer has been known as a disease consisting of several different types. Cancer is a life threatening disease in the world today. There are so many types of cancer in the world, one of which is colon cancer. Colon cancer is one of the number one killers in the world. However, because there isn’t any obvious symptom of colon cancer at an early stage, people do not realize that they suffer from it. Even though cancer formation is different for each type of cancer, it is still a big challenge to make cancer classification with good accuracy. Many machine learning has been applied to the data of human’s genes in order to get the most relevant genes in the classification of cancer. The author proposes the Naïve Bayes Classifier model as a classification method to show that the model has good accuracy, good precision, good recall, good f 1 — score in classifying the data of patients suffering from colon cancer or not. In this proposed model, Naïve Bayes Classifier is a technique prediction based on simple probabilistic and on the application of the Bayes theorem (or Bayes rule) with a strong independence assumption. Therefore, this model is able to make higher classification accuracy with less complexity. In particular, it achieves up to 95.24% classification accuracy, thus this model can be an efficient analysis tool.

Golden Ratio and Its Application to Bayes Classifier Based Face Sketch Gender Classification and Recognition

“…In structured peer-to-peer systems the authors of [30] present an intelligent cooperative web caching approaches based on J48 decision tree and Naïve Bayes (NB) supervised machine learning algorithms. In [31] authors use Naïve Bayes classifier models for predicting the colon cancer.…”

Acute sinusitis data classification using grey wolf optimization-based support vector machine

“…The previous research on support vector machine was proposed to process this data [4]. Also, many researches before using other methods of classification for classifying disease data, such as binary logistic regression for ovarian cancer classification [5], fast fuzzy clustering for breast cancer data [6], naive bayes classifier for predicting colon cancer [7], random forest classifier for predicting prostate cancer [8], and many more. Therefore, in this research, grey wolf optimization-support vector machine (GWO-SVM) will be the machine learning technique used, where the GWO technique will be used to tuned the parameters in SVM.…”

Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

“…Machine learning has been accepted for various classification problems in various fields, one of which is in the field of medicine. In the field of medicine, several machine learning methods are used to detect several types of cancer, namely breast cancer [8][9][10][11], cervical cancer [12,13], ovarian cancer [14,15], colon cancer [16], prostate cancer [17], and lung cancer [18].…”