Breast Cancer Diagnosis by Different Machine Learning Methods Using Blood Analysis Data

Abstract

Today, one of the most common types of cancer is breast cancer. It is crucial to prevent the propagation of malign cells to reduce the rate of cancer induced mortality. Cancer detection must be done as early as possible for this purpose. Machine Learning techniques are used to diagnose or predict the success of treatment in medicine. In this study, four different machine learning algorithms were used to early detection of breast cancer. The aim of this study is to process the results of routine blood analysis with different ML methods and to understand how effective these methods are for detection. Methods used can be listed as Artificial Neural Network (ANN), standard Extreme Learning Machine (ELM), Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). Dataset used were taken from UCI library. In this dataset age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin and chemokine monocyte chemoattractant protein 1 (MCP1) attributes were used. Parameters that have the best accuracy values were found by using four different Machine Learning techniques. For this purpose, hyperparameter optimization method was used. In the end, the results were compared and discussed.

Breast Cancer–Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine

“…Extreme learning machine [78] 80.00% PySpark and its machine learning frameworks [79] 83.0% Classification via regression [80] 80.0% Proposed model 86.97% Table A3) was 86.97%, as applied to the BCCD data set. Furthermore, a comparison of the results of six methods (Table II) using the same data set suggested that the proposed method is clearly superior.…”

CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection

“…In literature, there are many studies about artificial intelligence employed for various purposes like Alzheimer’s disease diagnosis, cancer estimation, biopsy and dermoscopy analysis etc. [31] , [32] , [33] , [34] , [35] . In recent times, the COVID-19 pandemic creates heavy work on health workers.…”

Diagnosis of Breast Cancer Using Improved Machine Learning Algorithms Based on Bayesian Optimization

“…In recent years, many different approaches have been tried in the literature for the detection of breast cancer using the BCC dataset ( Table 1). The results of the BO-KNN model were compared with the models developed by Patrício et al [17], Li [18], Aslan et al [19], Singh [4], Akben [20], and Silva Araújo et al [21]. In all of these studies, the same number of subjects (116) were used with different input combinations and data division strategies.…”

“…Assiri and Kamel [16] examined the serum leptin, resistin and visfatin levels as risk factors for postmenopausal breast cancer. As shown in Table 1, extensive studies have been conducted on the Breast Cancer Coimbra (BCC) dataset in recent years [4,[17][18][19][20][21]. Patrício et al [17] constructed LR, random forest (RF), and support vector machine models (SVM) models using glucose, age, resistin, and BMI features.…”

“…Li [18] used five different classification models including RF, SVM, decision tree (DT), artificial neural network (ANN) and logistics regression (LR) for breast cancer detection using glucose, insulin, homeostasis model assessment (HOMA), chemokine monocyte chemoattractant protein 1 (MCP-1), leptin, adiponectin, resistin, age and BMI data. Differently from this study, Aslan et al [19] developed an extreme learning machine (ELM) and KNN models for classification using the same dataset. Singh [4] improved various machine learning models with different feature selection and data division strategies using clinical and anthropometric features.…”