A new medical decision making system: Least square support vector machine (LSSVM) with Fuzzy Weighting Pre-processing

2007 ◽  
Vol 32 (2) ◽  
pp. 409-414 ◽  
Author(s):  
Emre Çomak ◽  
Kemal Polat ◽  
Salih Güneş ◽  
Ahmet Arslan
Keyword(s):  
Decision Making ◽  
Support Vector Machine ◽  
Medical Decision Making ◽  
Least Square ◽  
Medical Decision ◽  
Support Vector ◽  
Decision Making System

scholarly journals Computer-assisted Medical Decision-making System for Diagnosis of Urticaria

2016 ◽  
Vol 1 (1) ◽  
pp. 238146831667775 ◽  
Author(s):  
Jabez J. Christopher ◽  
Harichandran Khanna Nehemiah ◽  
Kannan Arputharaj ◽  
George L. Moses
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Computer Assisted ◽  
Decision Making System

Case Study of the Medical Decision-Making System ETL Abnormalities Processing Procedure

2012 ◽  
Vol 433-440 ◽  
pp. 894-899
Author(s):  
Hui Chen Tsai ◽  
Kuo Chung Lin ◽  
Ching Long Yeh
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Process Data ◽  
Customer Complaint ◽  
Decision Making System ◽  
Restoration Mechanism ◽  
Data Content ◽  
Content System

The primary purpose of this research is to resolve the problem of ETL operation failure in execution of ETL (Extraction, Transformation and Loading) by the medical decision-making system due to data content, system factors and defective program design, thereby affect online daily operation of the application system and even customer complaint. This research first research and develop how to record in database, whether successful or not, the number of ETL file conversion program (including tool and self-wrote PL/SQL program) execution process, data status, and execution time; followed by designing control mechanism and write Script for voluminous table restoration for automatic execution by the system; afterwards followed by research and develop system automatic execution of restoration to stop only the affected application programs of the table and design a restoration mechanism. Lastly, through verification, this R&D result would correctly restore the data and table required by ETL procedure.

scholarly journals CANCER PROGNOSTIC EVALUATION VIA SUPPORT VECTOR MACHINES

2014 ◽  
pp. 29-34
Author(s):  
Domenico Conforti ◽  
Domenico Costanzo ◽  
Rosita Guido
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Binary Classification ◽  
Classification Problem ◽  
Good Prognosis ◽  
Medical Decision ◽  
Support Vector ◽  
Breast Cancer Patients ◽  
Decision Making Problem

In this paper we considered a very challenging medical decision making problem: the short-term prognosis evaluation of breast cancer patients. In particular, the oncologist has to predict the more likely outcome of the disease in terms of survival or recurrence after a given follow-up period: “good” prognosis if the patient is still alive and has not recurrence after the follow-up period, “poor” prognosis if the patient has recurrence or dies within the follow-up period. This prediction can be realized on the basis of the execution of specific clinical tests and patient examinations. The relevant medical decision making problem has been formulated as a supervised binary classification problem. By the framework of generalized Support Vector Machine models, we tested and validate the behavior of four kernel based classifiers: Linear, Polynomial, Gaussian and Laplacian. The overall results demonstrate the effectiveness and robustness of the proposed approaches for solving the relevant medical decision making problem.

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