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Study on Detection of Breast Cancer

   

Added on  2020-05-11

4 Pages665 Words193 Views
Running head: BIOINFORMATICSBioinformaticsName of the StudentName of the UniversityAuthor note
Study on Detection of Breast Cancer_1
1BIOINFORMATICSBreast cancer is the second most common cancer death cause among the women. Theearly diagnosis and detection of breast cancer until 5 years the survival rates increases from 56%to more than 86%. Therefore, correct tumour diagnosis is important for the treatment of breastcancer. However, physicians face complex issues during the diagnosis procedure and so, modernmedical data diagnostic methods are used for the clinical observation. Thus, reliable and accuratediagnosis system is necessary for the early detection and diagnosis of breast cancer (benign andmalignant). Hence, models are being designed for the diagnosis and differentiation of benign andmalignant tumours. The paper by Montazeri et al., (2016) proposed a model called machine learning modelbased on rule-based classification method for the different types of breast cancer survivalprediction. For the study, the method used was a dataset of eight attributes that included therecord of 900 patients having 876 females and 24 males and data collected from Cancer RegistryOrganization of Kerman Province, in Iran. In this study, seven machine learning techniques likeNaive Bayes (NB), 1-Nearest Neighbor (1NN), Trees Random Forest (TRF), Support VectorMachine (SVM), Multilayer Perceptron (MLP) and RBF Network (RBFN) and AdaBoost (AD)with 10-cross fold technique was used for the breast cancer survival prediction. The performanceof these machine-learning techniques was evaluated with great precision, accuracy, specificity,sensitivity and area under the ROC curve. The three techniques called Bagging, Random andBoosting subspace were used for the detection of heart valve disorders and adaptive and rapiddiagnostic systems based on Learning Vector Quantization (LVQ) artificial neural network werepresented. A reinforcement mechanism was used for increasing the success rate of diagnosticmethods and reduction in decision time. For the extraction of useful information and detection oftumors, support vector machine (K-SVM) algorithm and tools combination was presented. K-
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