Multi-Criteria Decision Support System for Lung Cancer Prediction

Abstract-- Lung cancer is leading cause of death in the cancer group. In general, lung cancer has some symptoms, but at an early stage, symptoms are not perceived by the patient. As a result, when patients go to hospital, lung cancer has been diagnosed in middle or high stage. For early detection of lung cancer, necessary a decision support system based on computerized technology that can be utilized by doctor needed to detection lung cancer. The clinical decision support system will help to determine specific medical treatment. The clinical decision support system capable to know data input and produce output result by learning process. The learning process is part of process in artificial neural network (ANN). Many methods used in ANN as Backpropagation (BP)learning algorithm. BP used to produce output result in decision support system. Keywords-- lung cancer, stage, clinical decision support systems, neural network, multilayer perceptron, backpropagation algorithm

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Analysis of the implementation of a multi-scenario decision support system in the treatment of lung cancer

Technology audit and production reserves ◽

10.15587/2706-5448.2021.238846 ◽

2021 ◽

Vol 5 (2(61)) ◽

pp. 33-38

Author(s):

Yevhen Artamonov ◽

Viktoria Borisevich ◽

Iurii Golovach

Keyword(s):

Lung Cancer ◽

Decision Support ◽

Decision Support System ◽

Decision Support Systems ◽

Support System ◽

Support Systems ◽

Gradual Transition ◽

Research Attention ◽

Intelligent Interface ◽

Automation Systems

The object of the research is the decision support system in the treatment of lung cancer, the subject of the research – the use of a multi-scenario interface in the construction of decision support systems. One of the problem areas in software development is the need for multi-criteria adaptation of interfaces to users. This problem became especially acute after the introduction of quarantine when various automation systems began to develop rapidly, aimed at reducing direct contact between the customer and the service provider. If earlier software users were the more or less related group, now the difference began not only at the level of technical qualifications. Now, when developing software, more attention should be paid to physiological and psychological differences between users, features of hardware and software, environment, and other criteria. In the current situation, it turned out that in most cases automated systems are used by persons who are not interested in these systems but simply have to use them. One of the options for solving this problem is to create an adaptive universal interface. This research is aimed at analyzing methods for implementing multi-scenario decision support systems in the treatment of lung cancer. In the research, attention is paid to the following aspects: adaptive intelligent interface, architecture and structure of the adaptive intelligent interface, algorithms for the functioning of agents of adaptive system interfaces. In the research, the system was used by 500 participants for 30 days. The benchmark was the type of data display scenario selected at the start and end of the day. The research showed a gradual transition of users to scenarios of higher complexity, which involve the analysis of all available information. The tendency of reverse transitions decreases with time, and from the 18th day of using the system, the type of the selected interface changes in rare moments. These results proved the possibility of using automatically configurable interfaces, and bringing them to the final form will be achieved in 18–20 days of using the system.

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Evaluation of a Decision-Support System for Inoperable Non-Small Cell Lung Cancer

Methods of Information in Medicine ◽

10.1055/s-0038-1635040 ◽

1994 ◽

Vol 33 (04) ◽

pp. 397-401 ◽

Cited By ~ 4

Author(s):

P. Kolari ◽

T. Wigren

Keyword(s):

Lung Cancer ◽

Decision Support ◽

Decision Support System ◽

Cell Lung Cancer ◽

Support System ◽

Palliative Radiotherapy ◽

Treatment Decision ◽

Survival Times ◽

Treatment Groups ◽

Therapeutic Decision Making

Abstract:The purpose of this study was to find out whether a decision-support system is able to assist a clinician in predicting patient outcome and in selecting optimal treatment in oncology. The domain of the evaluated decision-support prototype was primary therapeutic decision making in inoperable non-smali cell lung cancer. The performance of the prototype was tested on retrospective material consisting of 112 patients treated by radiotherapy. Survival was the endpoint for examining whether the treatment decision proposed by the system was more accurate than the decision actually made by the clinician. Certain prognostic variables were used by the system to classify patients into two treatment groups, radical or palliative radiotherapy. The median survival times of these groups were 15 and 7 months, respectively, compared with 9 and 8 months in the corresponding groups classified by the clinician. Our results indicate that clinicians need support in treatment selection and that decision-support systems could be a potential answer.

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