Cervical Cancer Cell Prediction using Machine Learning Classification Algorithms

In cancer identification, extrapolation of cervical cancer in patients shows a vital role. To save people from the cancer field of cancer detection, machine learning can play a big role in saving lives. In this paper, to make the detection process a portion faster and accurate machine learning techniques such as Decision Stump, C4.5 and Averaged One Dependence Estimators (AODE) for novel NCBI cervical cancer data set are made. Classification and Regression Tree (CART) is a simple decision tree algorithm that is used to create a decision tree of a given set. Here, a top-down Greedy search is used in order to check each attribute at every tree node. For building a Decision Stump algorithm, a decision tree which consists of nodes and an arc that connects nodes with the Entropy concept is used. The extension of the basic Decision Stump algorithm is the C4.5 algorithm on selecting the optimal split it recursively visits each decision node. The process gets continued until there is no further split is possible. In this way, the prediction is possible for the given data set. Bayesian methods are those that explicitly apply Bayes' Theorem for problems such as classification algorithms acts as a quick method for the creation of a statistical predictive model. AODE are based on the Bayesian theorem which is commonly used to solve prediction problems for ease usage in the medical field. In this research Decision Stump, C4.5, and AODE are implemented with help of the training set. The basic designs are used to predict whether a feminine is having cervical cancer or not.