scholarly journals A practical implementation of machine learning in predicting breast cancer

2021 ◽  
Vol 1 (2) ◽  
pp. 16-23
Author(s):  
Ajna Fetić ◽  
Adnan Dželihodžić

Cancer is the leading disease in the world by the increasing number of new patients and deaths every year. Hence, it is the most feared disease of our time. It is believed that lung cancer and breast cancer are most common types of cancer and they both are subtypes of the same group of cancer – carcinoma. With this type of cancer early detection is of great importance for patient survival. As it is the disease that has unfortunately been around for many years, today we have datasets with all necessary information for diagnosing and predicting cancer. Predicting cancer means deciding if the cancer is malignant or benign. The key to this answer lays in different values of parameters that have been stored when the disease was discovered. Machine learning plays the crucial role in predicting cancer, given the fact that algorithms such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and etc. are designed to find the pattern that occurs in large sets of data and based on that make a decision. In this paper, author's goal is to see how machine learning and its practical implementation on public datasets can help with early breast cancer diagnosis and hopefully help save more lives.

2020 ◽  
Vol 14 ◽  

Breast Cancer (BC) is amongst the most common and leading causes of deaths in women throughout the world. Recently, classification and data analysis tools are being widely used in the medical field for diagnosis, prognosis and decision making to help lower down the risks of people dying or suffering from diseases. Advanced machine learning methods have proven to give hope for patients as this has helped the doctors in early detection of diseases like Breast Cancer that can be fatal, in support with providing accurate outcomes. However, the results highly depend on the techniques used for feature selection and classification which will produce a strong machine learning model. In this paper, a performance comparison is conducted using four classifiers which are Multilayer Perceptron (MLP), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest on the Wisconsin Breast Cancer dataset to spot the most effective predictors. The main goal is to apply best machine learning classification methods to predict the Breast Cancer as benign or malignant using terms such as accuracy, f-measure, precision and recall. Experimental results show that Random forest is proven to achieve the highest accuracy of 99.26% on this dataset and features, while SVM and KNN show 97.78% and 97.04% accuracy respectively. MLP shows the least accuracy of 94.07%. All the experiments are conducted using RStudio as the data mining tool platform.


The Breast Cancer is disease which tremendously increased in women’s nowadays. Mammography is technique of low-powered X-ray diagnosis approach for detection and diagnosis of cancer diseases at early stage. The proposed system shows the solution of two problems. First shows to detect tumors as suspicious regions with a weak contrast to their background and second shows way to extract features which categorize tumors. Hence this classification can be done with SVM, a great method of statistical learning has made significant achievement in various field. Discovered in the early 90’s, which led to an interest in machine learning? Here the different types of tumor like Benign, Malignant, or Normal image are classified using the SVM classifier. This techniques shows how easily we can detect region of tumor is present in mammogram images with more than 80% of accuracy rates for linear classification using SVM. The 10-fold cross validation to get an accurate outcome is been used by proposed system. The Wisconsin breast cancer diagnosis data set is referred from UCI machine learning repository. The considering accuracy, sensitivity, specificity, false discovery rate, false omission rate and Matthews’s correlation coefficient is appraised in the proposed system. This Provides good result for both training and testing phase. The techniques also shows accuracy of 98.57% and 97.14% by use of Support Vector Machine and K-Nearest Neighbors


2021 ◽  
Vol 12 (4) ◽  
pp. 117-137
Author(s):  
Mazen Mobtasem El-Lamey ◽  
Mohab Mohammed Eid ◽  
Muhammad Gamal ◽  
Nour-Elhoda Mohamed Bishady ◽  
Ali Wagdy Mohamed

There are many cancer patients, especially breast cancer patients as it is the most common type of cancer. Due to the huge number of breast cancer patients, many breast cancer-focused hospitals aren't able to process the huge number of patients and might expose some women to late stages of cancer. Thus, the automation of the process can help these hospitals in speeding up the process of cancer detection. In this paper, the authors test several machine learning models such as k-nearest neighbours (KNN), support vector machine (SVM), and artificial neural network (ANN). They then compare their accuracies and losses with themselves and other models that have been developed by other researchers to see whether their approach is efficient or not and to decide what machine learning algorithm is best to use.


Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 919
Author(s):  
Isaac Daimiel Naranjo ◽  
Peter Gibbs ◽  
Jeffrey S. Reiner ◽  
Roberto Lo Gullo ◽  
Caleb Sooknanan ◽  
...  

The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018–March 2020; Medical University Vienna, from January 2011–August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7–99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70–0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75–0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77–0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0–88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.


2021 ◽  
Vol 30 (1) ◽  
pp. 998-1013
Author(s):  
Shubham Vashisth ◽  
Ishika Dhall ◽  
Garima Aggarwal

Abstract The rapid pace of development over the last few decades in the domain of machine learning mirrors the advances made in the field of quantum computing. It is natural to ask whether the conventional machine learning algorithms could be optimized using the present-day noisy intermediate-scale quantum technology. There are certain computational limitations while training a machine learning model on a classical computer. Using quantum computation, it is possible to surpass these limitations and carry out such calculations in an optimized manner. This study illustrates the working of the quantum support vector machine classification model which guarantees an exponential speed-up over its typical alternatives. This research uses the quantum SVM model to solve the classification task of a malignant breast cancer diagnosis. This study also demonstrates a comparative analysis of distinct forms of SVM algorithms concerning their time complexity and performances on standard evaluation metrics, namely accuracy, precision, recall, and F1-score, to exemplify the supremacy of quantum SVM over its conventional variants.


2021 ◽  
Author(s):  
Woldson Leonne Pereira Gomes ◽  
Antonio Silveira

Breast cancer is a more common neoplasm among women (not considering non-melanoma skin cancer). The estimate for the coming years is still growing and poses a threat to human health. Currently, the methods used in the diagnosis of breast cancer are performed through analysis of mammography images. Allowed, an analysis made by two specialists, which are subject to errors due to factors such as fatigue and lack of capacity. Not only the factor of human errors in diagnoses, certainly the long periods of time until the final diagnosis is another factor to be taken into account, because cancer is a progressive disease over time. In this sense, the present work applied a solution through the automatic classification of mammography images, in order to determine as normal or cancer. In addition, for simulations, two machine learning techniques were added independently, as they can eventually serve as a support in the diagnosis of breast cancer, that is, a CAD system, which means “computer-aided diagnosis”. As machine learning techniques applied for classification referenced as convolutional neural networks and support vector machines. Subsequently, the construction of the classification algorithms, they were subjected to the testing phase, which was found to be more than 85% accurate in the classification of mammography images.


2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Dongquan Chen ◽  
Yufeng Li ◽  
Lizhong Wang ◽  
Kai Jiao

Breast cancer (BC) is the second most common cancer diagnosed in American women and is also the second leading cause of cancer death in women. Research has focused heavily on BC metastasis. Multiple signaling pathways have been implicated in regulating BC metastasis. Our knowledge of regulation of BC metastasis is, however, far from complete. Identification of new factors during metastasis is an essential step towards future therapy. Our labs have focused on Semaphorin 6D (SEMA6D), which was implicated in immune responses, heart development, and neurogenesis. It will be interesting to know SEMA6D-related genomic expression profile and its implications in clinical outcome. In this study, we examined the public datasets of breast invasive carcinoma from The Cancer Genome Atlas (TCGA). We analyzed the expression of SEMA6D along with its related genes, their functions, pathways, and potential as copredictors for BC patients’ survival. We found 6-gene expression profile that can be used as such predictors. Our study provides evidences for the first time that breast invasive carcinoma may contain a subtype based on SEMA6D expression. The expression of SEMA6D gene may play an important role in promoting patient survival, especially among triple negative breast cancer patients.


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