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The models and methods of an assessment of various types of survival in practice of the children's oncologist

Student: Pavel Kondrat`ev

Supervisor:

Educational Programme: Bachelor

<p><strong>Abstract</strong></p><p>&nbsp;</p><p>One of the main conditions for subsequent chemotherapy and adjustments of further clinical research is a comprehensive analysis of the results of treatment. In the area of health statistics this analysis is called as survival analysis.</p><p>Nowadays there are few software tools that would allow researcher to quickly load the patient database and make a comprehensive analysis of efficiency of the conducted research. According to this there was developed a program with a graphical interface which allows researcher to read information and conduct analysis of survival by various methods.</p><p>Applied scope in this paper is lymphoblastic leukemia, a type of cancer that occurs most frequently in children.</p><p>The objective of this work are the patients subjected to chemotherapy in the treatment lymphoblastic leukemia.</p><p>The subject of research is to evaluate the effectiveness of methods used for analysis of survival.</p><p>Methodological basis of this work are methods of medical statistics and machine learning.</p><p>The general goal of this paper is the realization of mathematical model in the form of a program that will allows to use methods of survival analysis on input databases. &nbsp;In addition, the paper presents an experiment - consideration of competing risks as a problem of classification.</p><p>In the course of the experiment there was attempted application of machine learning techniques to predict events. There were considered three methods of classification in this work - support vector machine (SVM) method, naive Bayes method and the method of decision trees. High performance showed&nbsp; &nbsp;support vector method and trees method which gave a good accuracy of the prediction. Due to classifier of trees which has the property to build clear rules for the occurrence of the event, we got a good class of events with high precision. Using this class in the Cox regression model we bring a new, statistically significant risk factor &quot;Age&quot;.</p>

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