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

Bakhan Tofiq Ahmed


Article history:
Received 12.6.2020
Revised 16.9.2020
Accepted 29.12.2020
Today, cancer is counted as a riskier disease than other diseases in the globe. There are many cancer forms like leukemia, skin cancer, stomach cancer, etc, but Lung and Breast cancer are the most common forms that many people suffered from. Cancer is a disease in that cells have grown rapidly and abnormally that is why treating them is somehow tough in some cases but it can be controlled if they detect in their initial stage. Data-Mining Classification Algorithms had a vital role in predicting and recognizing both benign and malignant cells. Several classifiers are available to classify the usual and unusual cells such as Decision-Tree, Artificial-Neural Net, Support Vector Machine, K-Nearest Neighbor, etc. This paper presents a systematic review of the most well-known Data-Mining Classification algorithms for Lung and Breast Cancer Diagnose. A brief review of KDD and the Data-Mining concept has been demonstrated. The D-Tree, A-NN, SVM, and Naïve Bayes classifiers that are widely utilized in the biomedical field have been reviewed along with 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.


KDD, ANN, Support Vector Machine, Decision Tree, Naïve Bayes

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