scholarly journals Big Medical Data Decision-Making Intelligent System Exploiting Fuzzy Inference Logic for Prostate Cancer in Developing Countries

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 2348-2363 ◽  
Author(s):  
Kanghuai Liu ◽  
Zhigang Chen ◽  
Jia Wu ◽  
Yanlin Tan ◽  
Leilei Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Developing Countries ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Medical Data

scholarly journals An intelligent decision-making support system for the detection and staging of prostate cancer in developing countries

2020 ◽  
Author(s):  
Jun Zhang ◽  
Zhigang Chen ◽  
Jia Wu ◽  
Kanghuai Liu
Keyword(s):  
Prostate Cancer ◽  
Experimental Data ◽  
Decision Making ◽  
Developing Countries ◽  
Intelligent System ◽  
Medical System ◽  
Image Feature ◽  
Final Decision ◽  
Medical Systems ◽  
Cancer Data

Abstract Background Most developing countries face great problems in the medical, and the threat posed by large populations, scarce medical resources and inadequate medical personnel will affect the development and stability of the society. Therefore, for most developing countries, the development of intelligent medical systems can greatly alleviate the social contradictions arising from this problem. Intelligent Medical System is a system to assist doctors to make decisions. Through effective input, the system makes corresponding decisions and provides doctors with corresponding auxiliary diagnosis and relevant treatment plans. Methods The experimental team collected and compiled about 2,156,528 data items based on the hospitalization information of more than 8,000 prostate patients in three hospitals of Xiangya. The ratio of experimental data to test data was about 7: 3. Combining prostate cancer images and physical indicators to assist diagnosis and make decisions, from the extraction of corresponding image feature data to the final diagnosis decision, a new intelligent system for prostate cancer data decision-making based on Perceptron neural network is proposed. In the comprehensive decision-making, the final decision is made by using the rule-reasoning model. Through data collection, medical data analysis and integration, image processing and analysis, and disease detection and decision-making processes, patients are assisted in diagnosis and treatment to solve the problems and social contradictions facing most developing countries. Results Through the study of hospitalization information of more than 8,000 prostate patients in three hospitals, about 2,156,528 data items were collected and compiled for experiment purposes. Experimental data shows that when the patient base increases from 200 to 8,000, the accuracy of the machine-assisted diagnostic system will increase from 61% to 87%, and the doctor's diagnosis rate will be reduced to 81%. Conclusions From the study, it is concluded that when the patient base reaches a certain number, the diagnostic accuracy of the machine-assisted diagnosis system will exceed the doctor's expertise. So the smart medical system can help doctors and Medical experts make suggest more effective treatments.

scholarly journals An Intelligent Decision-Making Support System for the Detection and Staging of Prostate Cancer in Developing Countries

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Jun Zhang ◽  
Zhigang Chen ◽  
Jia Wu ◽  
Kanghuai Liu
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Developing Countries ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Diagnostic System ◽  
Medical Personnel ◽  
Medical Systems ◽  
Intelligent Decision Making ◽  
Diagnosis Rate

Most developing countries face huge challenges in the medical field; scarce medical resources and inadequate medical personnel will affect the development and stability of the society. Therefore, for most developing countries, the development of intelligent medical systems can greatly alleviate the social contradictions arising from this problem. In this study, a new data decision-making intelligent system for prostate cancer based on perceptron neural network is proposed, which mainly makes decisions by associating some relevant disease indicators and combining them with medical images. Through data collection, analysis and integration of medical data, as well as the disease detection and decision-making process, patients are given an auxiliary diagnosis and treatment, so as to solve the problems and social contradictions faced by most developing countries. Through the study of hospitalization information of more than 8,000 prostate patients in three hospitals, about 2,156,528 data items were collected and compiled for experiment purposes. Experimental data shows that when the patient base increases from 200 to 8,000, the accuracy of the machine-assisted diagnostic system will increase from 61% to 87%, and the doctor’s diagnosis rate will be reduced to 81%. From the study, it is concluded that when the patient base reaches a certain number, the diagnostic accuracy of the machine-assisted diagnosis system will exceed the doctor’s expertise. Therefore, intelligent systems can help doctors and medical experts treat patients more effectively.

scholarly journals Robustness of Adaptive Neuro- Fuzzy Inference System for Optimal Prediction using Roulette Wheel Method

2020 ◽  
Vol 8 (5) ◽  
pp. 380-384
Keyword(s):  
Decision Making ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Optimal Prediction ◽  
Data Set ◽  
Inference System ◽  
Roulette Wheel ◽  
Neuro Fuzzy ◽  
Support Decision Making

An information system that supports automatic decision making with help of intelligent system by computerized manner. The proposed work has been developed and deployed a robust method is contributed to decision making in medical system and the diagnosis the major risk of the patients in earlier. The main goal of the proposed research is to develop data mining techniques to support decision making and to control the controllable risk factors and also overcome the other parts of organs highly affected by diabetes, kidney disease, heart condition and which in turn reduces the risk of the patients. Robustness of Adaptive Neuro-Fuzzy Inference System (RANFIS) designed a fuzzy inference system (FIS) to enrich the knowledge about the data set whose membership function parameters can be altered randomly by the process of mutation.

IMPLEMENTASI PENENTUAN ARSITEKTUR MASJID MODERN DI ACEH MENGGUNAKAN PEMODELAN SISTEM CERDAS

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Soraya Masthura Hasan ◽  
T Iqbal Faridiansyah
Keyword(s):  
Fuzzy Inference System ◽  
Architectural Design ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Selection Process ◽  
System Modeling ◽  
Inference System ◽  
Normal Limits ◽  
Roof Structure ◽  
Selection Of

Mosque architectural design is based on Islamic culture as an approach to objects and products from the Islamic community by looking at their suitability and values and basic principles of Islam that explore more creative and innovative ideas. The purpose of this system is to help the team and the community in seeing the best mosque in the top order so that the system can be used as a reference for the team and the community. The variables used in the selection of modern mosques include facilities and infrastructure, building structure, roof structure, mosque area, level of security and facilities. The system model used is a fuzzy promethee model that is used for the modern mosque selection process. Fuzzy inference assessment is used to determine the value of each variable so that the value remains at normal limits. Fuzzy values will then be included in promethee assessment aspects. The highest promethee ranking results will be made a priority for the best mosque ranking. This fuzzy inference system and promethee system can help the management team and the community in determining the selection of modern mosques in aceh in accordance with modern mosque architecture. Intelligent System Modeling System In Determining Modern Mosque Architecture in the City of Aceh, this building will be web based so that all elements of society can see the best mosque in Aceh by being assessed by all elements of modern mosque architecture.Keywords: Fuzzy inference system, Promethe, Option of  Masjid

Information support for expert evaluation of the states of the external environment

2020 ◽  
pp. 71-76
Author(s):  
Rasol Murtadha Najah
Keyword(s):  
Decision Making ◽  
Environmental Conditions ◽  
Fuzzy Inference ◽  
Environmental Parameters ◽  
Information Support ◽  
Expert Assessment ◽  
Fuzzy Variables ◽  
Decision Making Models ◽  
The Difference ◽  
State Of The Environment

This article discusses the application of methods to enhance the knowledge of experts to build a decision-making model based on the processing of physical data on the real state of the environment. Environmental parameters determine its ecological state. To carry out research in the field of expert assessment of environmental conditions, the analysis of known works in this field is carried out. The results of the analysis made it possible to justify the relevance of the application of analytical, stochastic models and models based on methods of enhancing the knowledge of experts — experts. It is concluded that the results of using analytical and stochastic objects are inaccurate, due to the complexity and poor mathematical description of the objects. The relevance of developing information support for an expert assessment of environmental conditions is substantiated. The difference of this article is that based on the analysis of the application of expert methods for assessing the state of the environment, a fuzzy logic adoption model and information support for assessing the environmental state of the environment are proposed. The formalization of the parameters of decision-making models using linguistic and fuzzy variables is considered. The formalization of parameters of decision-making models using linguistic and fuzzy variables was considered. The model’s description of fuzzy inference is given. The use of information support for environment state assessment is shown on the example of experts assessing of the land desertification stage.

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