Data mining techniques for lung and breast cancer diagnosis: A review

Abstract

<p>Today, cancer counted as the riskier disease than the other diseases in the globe. There are many cancer forms like leukemia, skin cancer, and stomach cancer but lung and breast cancer are the most common forms that many people suffered from. Cancer is the disease that cell has grown rapidly and abnormally that is why treating it is somehow tough in some cases but it can be controlled if it is detected in the initial stage. Data-mining classification algorithms had a vital role in predicting and recognizing both benign and malignant cell. Several classifiers are available to classify the usual and unusual cells such as decision-tree, artificial-neural net, SVM, and KNN. This paper presents a systematic review about the most well-known data-mining classification algorithms for lung and breast cancer diagnose. A brief review about KDD and the data-mining concept has demonstrated. The Decision-Tree (D-Tree), ANN, Support-vector-machine, and naïve Bayes classifier that is widely utilized in the biomedical field has been reviewed along with the some algorithms such as C4.5, Cart, and Iterative -Dichotomiser 3 ‘ID3’. A comparison has been done among various reviewed papers in terms of accuracy that used various data-mining classification algorithms to propose the lung and breast cancer diagnosis system. The experimental results of the reviewed papers showed that the Multilayer Perceptron (MLP) and Logistic Regression (LR) gave a higher accuracy of 99.04% and 98.1%, respectively.</p>

Automated breast cancer detection system from breast mammogram using deep neural network

“…In that 206 are normal images and 113 are breast cancer images [22]. The proposed approach is compared with other classifiers like Naive Bayes (NB) [23], k-nearest neighbor (KNN) [24], decision tree (DT) [23], support vector machine (SVM) [24] and random forest (RF) [25].…”

Evolutionary tree-based quasi identifier and federated gradient privacy preservations over big healthcare data

“…A hybrid algorithm was developed in [24] for increasing the security in e-business systems. Data-mining classification algorithms were introduced in [25] to detect the lung and breast cancer diagnose. New approach was developed in [26] to decision-making based on the characterization of cognitive tasks.…”

Vector support machine algorithm applied to the improvement of satisfaction levels in the acquisition of professional skills

“…University institutions play a decisive role within society, since they seek to educate people who manage to acquire skills, which will be used in their professional development, it is therefore very important to link the role of the university with knowledge management supported by artificial intelligence [13]- [15]. In this context of handling a large amount of data or what we know as data mining, an aspect to be highlighted is the generation of grouping or classification models of objects or entities based on the same characteristic or behavior, which will then be used for the decision making [16]- [18]. In [19]- [23] it is indicated that among the different algorithms used to perform predictive classification models we have the models generated through the vector support machine (VSM) algorithm.…”