The Hiba et al.  by using Machine learning algorithms for breast cancer and it uses breast cancer dataset, unlike othe r in this paper it compares the classification algorithms like KNN, C4.5, SVM and NB. By comparing those classifier SVM scores better accuracy 97.13% with lowest error rate.
The dataset used in the book is a modified version of the "Breast Cancer Wisconsin (Diagnostic) Data Set" from the UCI Machine Learning Repository 4 , as described in Chapter 3 ("*Lazy Learning - Clasification Using Nearest Neighbors") of the aforementioned book. You can get the modified...Keywords: Breast cancer; Bioinformatics; Logistic regression model; CUDA parallel programming; supervised algorithm; Machine learning; Data analysis. Large number of risk prediction models have been developed that evaluate different types of risk factors for breast cancer tumor and not only.Women carrying a breast-cancer-producing mutation in BRCA1 or BRCA2. Although a woman's risk may be accurately estimated, these predictions do not allow one to say precisely which woman will develop breast cancer.
Dec 22, 2020 · A Machine-Learning Framework for Accurate Classification and Quantification of Oncogenic Variants Using the QuantideX NGS DNA Hotspot 21 Kit Analytical Validation of the QuantideX NGS DNA Hotspot 21 Kit, A Diagnostic NGS System for the Detection of Actionable Mutations in FFPE Tumors This paper is restricted to the study of logistic regression for the classification of breast cancer using Wisconsin Breast Cancer Dataset (WBCD) from UCI machine learning online repository. Performance of this model is measured using precision score, recall score and f1-score only. 4 1.5 Paper organization This paper is broken into five chapters.
Jul 06, 2019 · Output : Cost after iteration 0: 0.692836 Cost after iteration 10: 0.498576 Cost after iteration 20: 0.404996 Cost after iteration 30: 0.350059 Cost after iteration 40: 0.313747 Cost after iteration 50: 0.287767 Cost after iteration 60: 0.268114 Cost after iteration 70: 0.252627 Cost after iteration 80: 0.240036 Cost after iteration 90: 0.229543 Cost after iteration 100: 0.220624 Cost after ... Abstract: Machine learning based lung cancer prediction models have been proposed to assist clinicians in managing incidental or screen detected indeterminate pulmonary nodules. Such systems may be able to reduce variability in nodule classification, improve decision making and ultimately reduce the number of benign nodules that are needlessly ...