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By Abdul Rasul 1, 2, † , Chayut Bunterngchit 1, 3, † , Luo Tiejian 1, * , Md. Ruhul Islam 4, Qiang Qu 2 and Qingshan Jiang 2, *
Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Pdf) Data Driven Decision Making
Received: December 6, 2021 / Revised: March 2, 2022 / Accepted: March 3, 2022 / Published: March 9, 2022
Breast cancer has a higher death rate than any other cancer in American women. Machine learning-based predictive models are early detection methods for breast cancer diagnosis. However, evaluating models that detect cancer effectively is still challenging. In this work, we proposed data mining techniques (DET) and developed four different prediction models to improve the accuracy of breast cancer diagnosis. Before the models, the necessary four-layer DET, i.e., feature distribution, correlation, elimination, and metaparameter optimization, was deep-dive to identify robust feature classification into malignant and benign classes. The proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Coimbra Breast Cancer (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were used to evaluate the efficiency and training time of each classifier. The diagnostic ability of the models improved with our DET, with polynomial SVM achieving 99.3%, LR with 98.06%, KNN with 97.35%, and EC with WDBC dataset with 97.61% accuracy. We compared our remarkable results with previous studies in terms of accuracy. The implementation method and findings can guide doctors to adopt an effective model for the practical understanding and prognosis of breast cancer tumors.
Breast cancer (BC) is the leading cause of death in women worldwide, after lung cancer, with 2,261,419 new cases and 684,996 new deaths in 2020 . In the United States, 281,550 new cases of breast cancer were diagnosed and 43,600 were reported in women in 2021 . Breast cancer is a type of cancer that originates in breast tissue, usually in the inner layer of the ducts or lobules that deliver milk to the ducts. Cancer cells arise from normal cells due to changes or mutations in deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). These changes or distortions may occur spontaneously as a result of an increase in entropy or may be caused by other factors. For example, electromagnetic radiation (X-rays, microwaves, ultraviolet rays, gamma rays, etc.), nuclear radiation, bacteria, viruses, fungi, parasites, chemicals in the air, heat, food, water , free radicals, mechanical damages of DNA and RNA at the cellular level, development and aging . Generally, benign and malignant are two types of cancer. Although not completely life-threatening and cancerous, it increases the risk of developing breast cancer. On the contrary, malignant tumors are more dangerous and cancerous. A study conducted on breast cancer diagnosis reported that 20% of women died of malignant cancer .
These studies emphasize the diagnosis of cancer, which has recently become a common biomedical problem. Researchers use data mining (DM) and machine learning (ML) technologies to predict breast cancer . Prediction models based on classification on DM and ML can limit diagnostic errors and improve the efficiency of cancer diagnosis. DM is a broad set of different approaches to discover hidden knowledge and information from large-scale data sets that are difficult to analyze directly. It has been widely used in the implementation of prognostic system for various diseases such as heart disease , lung cancer  and thyroid cancer . DM and ML techniques have been incorporated into breast cancer diagnosis with computer systems  and fuzzy genetics . The results of these studies successfully classify the characteristic tumors into two types by evaluating the classification and prediction of the incoming tumor based on the previous data.
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In the literature, a research study proved that predicting breast cancer with machine learning classifiers at an early stage not only increases the chance of survival, but also can control the spread of cancer cells in the body . For example, one study used a support vector machine (SVM)-based method for breast cancer detection and obtained practical results in prediction . Similarly, Fury et al.  used SVM to classify tumor tissue with a linear kernel and achieved an accuracy of 93.4%. Later, this work was extended by Zheng et al. (2014) proposed a hybrid K-SVM model to classify the Wisconsin Diagnostic Breast Cancer (WDBC) dataset and achieved an accuracy of 97% [ 14 ]. Meanwhile, some other researchers based on different classifications such as Seddik et al. (2015), proposed a method based on tumor variables for a binary logistic model to identify breast cancer WDBC data and obtain good results . Likewise, Merrett et al. A k-nearest neighbor (KNN) classifier was used to predict breast cancer by designing a feature reduction method with independent component analysis. calculated the feature distribution function by reducing one feature (1C) and 30 features and achieved an accuracy of 91% .
Apart from these favorable accuracies with different classifiers and methods, these aforementioned studies have not considered data mining techniques, and data mining techniques can be more powerful to achieve efficient performance. Due to the lack of such necessary techniques, various studies [16, 17, 18, 19] face the limitation of the accuracy of ML classifiers. Meanwhile, the confusion matrices misidentified the malignant and benign classes in those studies due to the incorrect prediction of the true negative and false negative matrices. Another shortcoming was found in those previous studies that used metrics to evaluate feature training with nonlinear classifiers. However, the runtime performance of the model increases rapidly with the number of features . As a result, the prediction model is slow and the detection accuracy is affected. In contrast, model accuracy and time complexity are important issues for the data analyst and clinician. These issues and findings as mentioned above prompted us to conduct a new study for breast cancer diagnosis by proposing data mining methods with different machine learning models.
In this research, four different prediction models were developed with four machine learning algorithms (SVM, KNN, logistic regression (LR) and ensemble classifier (EC)) and dealt with a large number of tumor features to extract essential information for them. Breast cancer diagnosis The aim was to discover an accurate and efficient prediction model for cancer classification using data mining techniques. This method proposes basic four-layer data mining (DET) techniques, including feature distribution, extraction, and metaparameter construction, for practical analysis of the Wisconsin Diagnostic Breast Cancer (WDBC) and Coimbra Breast Cancer Datasets (BCCD). These techniques were able to improve the accuracy of the machine learning predictive model and increase the diagnostic efficiency. In the absence of these techniques, we observed that some literature suffers from accuracy limitations. Although image data are more reasonable for breast cancer detection, we did not consider them in this work due to the WDBC and BCCD datasets targeted for the application of intelligent ML classifiers. It provides a framework through integration
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