Accuracy Of Decision Support System Research Articles

Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis.

For therapy planning in cancer patients multidisciplinary team meetings (MDM) are mandatory. Due to the high number of cases being discussed and significant workload of clinicians, Clinical Decision Support System (CDSS) may improve the clinical workflow. This review and meta-analysis aims to provide an overview of the systems utilized and evaluate the correlation between a CDSS and MDM. A total of 31 studies were identified for final analysis. Analysis of different cancers shows a concordance rate (CR) of 72.7% for stage I-II and 73.4% for III-IV. For breast carcinoma, CR for stage I-II was 72.8% and for III-IV 84.1%, P≤ 0.00001. CR for colorectal carcinoma is 63% for stage I-II and 67% for III-IV, for gastric carcinoma 55% and 45%, and for lung carcinoma 85% and 83% respectively, all P>0.05. Analysis of SCLC and NSCLC yields a CR of 94,3% and 82,7%, P=0.004 and for adenocarcinoma and squamous cell carcinoma in lung cancer a CR of 90% and 86%, P=0.02. CDSS has already been implemented in clinical practice, and while the findings suggest that its use is feasible for some cancers, further research is needed to fully evaluate its effectiveness.

Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique

Abstract In this research work, a user-friendly decision support framework is developed to analyze the behavior of Pakistani students in academics. The purpose of this article is to analyze the performance of the Pakistani students using an intelligent decision support system (DSS) based on the three-level machine learning (ML) technique. The neural network used a three-level classifier approach for the prediction of Pakistani student achievement. A self-recorded dataset of 1,011 respondents of graduate students of English and Physics courses are used. The ten interviews along with ten questions were conducted to determine the perception of the individual student. The chi-squared ( χ ) \left(\chi ) test was applied to test statistical significancy of the questionnaire. The statistical calculations and computation of data were performed by using the statistical package of IBMM SPSS version 21.0. The seven different algorithms were tested to improve the data classification. The Java-based environment was used for the development of numerous prediction classifiers. C4.5 algorithm shows the finest accuracy, whereas Naïve Bayes (NB) algorithm shows the least. The results depict that the classifier’s efficiency was improved by using a three-level proposed scheme from 83.2% to 88.8%. This prediction has shown remarkable results when compared with the individual level classifier technique of ML. This improvement in the accuracy of DSSs is used to identify more efficiently the gray areas in the education stratum of Pakistan. This will pave a path for making policies in the higher education system of Pakistan. The presented framework can be deployed on different platforms under numerous operating systems.

An approach to improve the accuracy of probabilistic classifiers for decision support systems in sentiment analysis

Social networks link people and machines, providing a huge amount of information that grows very fast without the possibility to be handled manually. Moreover, opinion mining is the process of using natural language processing, text analytics and computational linguistics to identify and extract subjective information in different sources such as social networks. To that, classification methods are used but due to the limitless number of topics and the breadth and ambiguity of natural language, with its peculiarities in social networks, the results can be greatly improved. In this work, we present DSociaL, a platform to automate the processing of information obtained from social networks, focusing on improving the accuracy of decision support systems for sentiment analysis. We focus on machine learning-based simple probabilistic classifiers, evaluating a naive Bayes classifier, the basis of one of the most used soft computing techniques. Thus, we show a use case in which the proposal, with definitions and refinements made by experts, helps to improve the prediction of users’ feelings towards a movie compared to what would happen with a conventional approach.

THE APPLICATION OF DECISION SUPPORT SYSTEM TO IDENTIFY CHILDREN WITH INTELLECTUAL DISABILITIES


 The research is intended to measure the accuracy of Decision Support System (DSS) to identify the characteristics of children with intellectual disabilities. A number of 40 teachers in Central Java, Indonesia were assigned as the sample comprising 16 special school teachers and 4 inclusive school teachers. As respondents, these teachers are required to (a) have a training on sofware application of DSS program of identification instrument, (b) have an experience on using DSS program, (c) have full commitment to the research activity. The data was collected from questionnaire and analized by quantitaive description. The reseacrh concluded that DSS program indicated the accuracy of 99,8%, and as many as 92.5% of teachers commented that DSS program was easy to use for identification of children with intellectual disabilities.

Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application

The model selection strategy is an important determinant of the performance and acceptance of a medical diagnostic decision support system based on supervised learning algorithms. This research investigates the potential of various selection strategies from a population of 24 classification models to form ensembles in order to increase the accuracy of decision support systems for the early detection and diagnosis of breast cancer. Our results suggest that ensembles formed from a diverse collection of models are generally more accurate than either pure-bagging ensembles (formed from a single model) or the selection of a “single best model.” We find that effective ensembles are formed from a small and selective subset of the population of available models with potential candidates identified by a multicriteria process that considers the properties of model generalization error, model instability, and the independence of model decisions relative to other ensemble members.