KEYWORDS: Breast cancer, Machine learning, Tumors, Tumor growth modeling, Diagnostics, Decision trees, Education and training, Data modeling, Support vector machines, Medicine
The goal of this research is to diagnose breast cancer using machine learning methods including decision trees, support vector machines (SVM), and naïve Bayesian classifiers. The properties of cell nuclei taken from breast biopsies are included in the Breast Cancer Wisconsin dataset, which is used in this study. Three different machine learning algorithms - naive Bayesian classifier, SVM and decision tree - are used to develop predictive models aimed at classifying samples as malignant or benign tumours. The study involves training the models on evaluation data and then evaluating their performance on test data. In order to compare the effectiveness of each method, many metrics like accuracy, sensitivity, and specificity are used in the evaluation of the results. The outcomes demonstrate the efficacy of every technique examined in this research. This work can be used as a springboard for future research into refining and customizing these techniques to intricate clinical situations, in addition to offering a useful comparison of the efficacy of three distinct machine learning approaches for breast cancer diagnosis. The results may facilitate the application of machine learning techniques in clinical settings, hence facilitating early detection and bettering the prognosis of patients with breast cancer.
The optimal organization of the territory of crop rotation fields and arrays allows you to choose the optimal scheme of cotton crop rotation. An analysis of the current level of development of crop rotations in the object under study showed that the agrotechnical foundations of cotton crop rotations have been studied quite fully, and specific recommendations have been developed for individual soil conditions of the region. At the same time, the organizational and economic substantiation of crop rotations has been little studied, taking into account the production and economic conditions of individual farms. This is especially true for the choice of crop rotation system, crop rotation and assessment of the yield of crop rotation fields, as well as linking the placement plan and crop rotation. Implementation allows you to determine in which field a particular crop should be sown. To solve this problem, a genetic algorithm is used. A computational experiment was carried out.
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