Breast Cancer Diagnosis (BCD) Model Using Machine Learning

Abstract

In the recent years, breast cancer research has made a significant growth however there is still a scope of advancement. Breast cancer increases the statistics of mortality among women. In concern to this issue, treatment of cancer should be started at the earlier stage, to increase the chances of survival of the patient. Thus, there is a need to diagnose breast cancer at the early stage using the features from the mammograms. This paper proposes an efficient BCD model to detect breast cancer by using Support Vector Machine (SVM) with 10-fold cross validation. The complexity of the problem increases if there are many input features for the diagnosis of cancer. Thus, Principal Component Analysis (PCA) is used to reduce the feature space from a higher dimension to a lower dimension. Experiment result shows that the PCA increases the accuracy of the model. The proposed BCD model is compared with other supervised learning algorithms like Decision trees (DT), Random Forest, k- Nearest Neighbors(k-NN), Stochastic Gradient Descent (SGD), AdaBoost, Neural Network (NN), and Naïve Bayes. Evaluation parameters like F1 measure, ROC curve, Accuracy, Lift curve and Calibration Plot proves that proposed BCD model outperforms and gives the highest accuracy among other compared algorithms

Breast Cancer Detection by Using Data Mining, a Review Study

“…[8]. Data mining and machine learning techniques are commonly used for detecting breast cancer in early stages, which can help to decrease mortality rate among breast cancer diseases [17]. These are explained briefly in the next subsections.…”

“…The results showed that the Naïve Bayes was more accurate, with less execution time than J48. The researchers in [17] proposed an efficient Breast Cancer Diagnosis (BCD) model to detect breast cancer by using a support vector machine (SVM) with 10-fold cross validation. The complexity of the problem increases if there are many input features used to diagnose breast cancer.…”

“…Naïve Bayes data mining technique was used alone or with other techniques as in [12], [14], [15], and [16]. Researchers used Support Vector Machine (SVM) with other techniques in [17], [18], and [19]. Regarding the results, the confusion matrix was used alone or with other evaluation techniques as in [13], [15], [17], and [19].…”

“…Researchers used Support Vector Machine (SVM) with other techniques in [17], [18], and [19]. Regarding the results, the confusion matrix was used alone or with other evaluation techniques as in [13], [15], [17], and [19]. Other evaluation techniques were used, like benchmark data set in [12], visual evaluation in [16], and comparative analysis in [20].…”

A Novel Master-Slave Architecture to Detect COVID-19 in Chest X-ray Image Sequences Using Transfer-Learning Techniques

“…Machine-learning and deep-learning methods are widely utilized for computer vision [ 11 , 12 ], image processing [ 13 ], early medical diagnosis [ 14 ], and indexing [ 15 ]. The literature review considers deep-learning and transfer-learning techniques for COVID-19 detection using several medical image modalities like CT scans, X-rays, etc.…”