scholarly journals Development of an Intelligent Decision Support System for Attaining Sustainable Growth within a Life Insurance Company

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1369
Author(s):  
Mohammad Farhan Khan ◽  
Farnaz Haider ◽  
Ahmed Al-Hmouz ◽  
Mohammad Mursaleen
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Life Insurance ◽  
Support Vector Machine Model ◽  
Sustainable Growth ◽  
Support Vector ◽  
Insurance Company ◽  
Life Insurance Company ◽  
Machine Model

Consumer behaviour is one of the most important and complex areas of research. It acknowledges the buying behaviour of consumer clusters towards any product, such as life insurance policies. Among various factors, the three most well-known determinants on which human conjecture depends for preferring a product are demographic, economic and psychographic factors, which can help in developing an accurate market design and strategy for the sustainable growth of a company. In this paper, the study of customer satisfaction with regard to a life insurance company is presented, which focused on comparing artificial intelligence-based, data-driven approaches to classical market segmentation approaches. In this work, an artificial intelligence-based decision support system was developed which utilises the aforementioned factors for the accurate classification of potential buyers. The novelty of this paper lies in developing supervised machine learning models that have a tendency to accurately identify the cluster of potential buyers with the help of demographic, economic and psychographic factors. By considering a combination of the factors that are related to the demographic, economic and psychographic elements, the proposed support vector machine model and logistic regression model-based decision support systems were able to identify the cluster of potential buyers with collective accuracies of 98.82% and 89.20%, respectively. The substantial accuracy of a support vector machine model would be helpful for a life insurance company which needs a decision support system for targeting potential customers and sustaining its share within the market.

scholarly journals An Evolutionary-Based Sentiment Analysis Approach for Enhancing Government Decisions during COVID-19 Pandemic: The Case of Jordan

2021 ◽  
Vol 11 (19) ◽  
pp. 9080
Author(s):  
Ruba Obiedat ◽  
Osama Harfoushi ◽  
Raneem Qaddoura ◽  
Laila Al-Qaisi ◽  
Ala’ M. Al-Zoubi
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Sentiment Analysis ◽  
Support System ◽  
Support Vector ◽  
Whale Optimization ◽  
Vector Machines ◽  
Standard Classification ◽  
The Government ◽  
F Measure

The world has witnessed recently a global outbreak of coronavirus disease (COVID-19). This pandemic has affected many countries and has resulted in worldwide health concerns, thus governments are attempting to reduce its spread and impact on different aspects of life such as health, economics, education, and politics by making emergent decisions and policies (e.g., lockdown and social distancing). These new regulations influenced people’s daily life and cast significant burdens, concerns, and disparities on various population groups. Taking the wrong actions and enforcing bad decisions by some countries result in increasing the contagion rate and more catastrophic results. People start to post their opinions and feelings about their government’s decisions on different social media networks, and the data received through these platforms present a very useful source of information that affects how governments perceive and cope with the current the pandemic. Jordan was one of the top affected countries. In this paper, we proposed a decision support system based on the sentiment analysis mechanism by combining support vector machines with a whale optimization algorithm for automatically tuning the hyperparameters and performing feature weighting. The work is based on a hybrid evolutionary approach that aims to perform sentiment analysis combined with a decision support system to study people’s posts on Facebook to investigate their attitudes and feelings toward the government’s decisions during the pandemic. The government regulations were divided into two periods: the first and latter regulations. Studying public sentiments during these periods allows decision-makers in the government to sense people’s feelings, alert them in case of possible threats, and help in making proactive actions if needed to better handle the current pandemic situation. Five different versions were generated for each of the two collected datasets. The results demonstrate the superiority of the proposed Whale Optimization Algorithm & Support Vector Machines (WOA-SVM) against other metaheuristic algorithms and standard classification models as WOA-SVM has achieved 78.78% in terms of accuracy and 84.64% in term of f-measure, while other standard classification models such as NB, k-NN, J84, and SVM achieved an accuracy of 69.25%, 69.78%, 70.17%, and 69.29%, respectively, with 64.15%, 62.90%, 60.51%, and 59.09% F-measure. Moreover, when comparing our proposed WOA-SVM approach with other metaheuristic algorithms, which are GA-SVM, PSO-SVM, and MVO-SVM, WOA-SVM proved to outperform the other approaches with results of 78.78% in terms of accuracy and 84.64% in terms of F-measure. Further, we investigate and analyze the most relevant features and their effect to improve the decision support system of government decisions.

Predictive modelling analysis for development of a radiotherapy decision support system in prostate cancer: a preliminary study

2017 ◽  
Vol 16 (2) ◽  
pp. 161-170 ◽  
Author(s):  
Kwang Hyeon Kim ◽  
Suk Lee ◽  
Jang Bo Shim ◽  
Kyung Hwan Chang ◽  
Yuanjie Cao ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Predictive Modelling ◽  
Radiotherapy Planning ◽  
Support Vector ◽  
Toxicity Prediction ◽  
Planning Decision

AbstractPurposeThe aim of this study is to develop predictive models to predict organ at risk (OAR) complication level, classification of OAR dose-volume and combination of this function with our in-house developed treatment decision support system.Materials and methodsWe analysed the support vector machine and decision tree algorithm for predicting OAR complication level and toxicity in order to integrate this function into our in-house radiation treatment planning decision support system. A total of 12 TomoTherapyTM treatment plans for prostate cancer were established, and a hundred modelled plans were generated to analyse the toxicity prediction for bladder and rectum.ResultsThe toxicity prediction algorithm analysis showed 91·0% accuracy in the training process. A scatter plot for bladder and rectum was obtained by 100 modelled plans and classification result derived. OAR complication level was analysed and risk factor for 25% bladder and 50% rectum was detected by decision tree. Therefore, it was shown that complication prediction of patients using big data-based clinical information is possible.ConclusionWe verified the accuracy of the tested algorithm using prostate cancer cases. Side effects can be minimised by applying this predictive modelling algorithm with the planning decision support system for patient-specific radiotherapy planning.

Diagnosis and Classification of Chronic Renal Failure Utilising Intelligent Data Mining Classifiers

2014 ◽  
Vol 9 (4) ◽  
pp. 1-12 ◽  
Author(s):  
Abeer Y. Al-Hyari ◽  
Ahmad M. Al-Taee ◽  
Majid A. Al-Taee
Keyword(s):  
Data Mining ◽  
Renal Failure ◽  
Decision Support ◽  
Chronic Renal Failure ◽  
Decision Support System ◽  
Support System ◽  
Clinical Decision ◽  
Support Vector ◽  
Artificial Neural Network Ann ◽  
Sensitivity Specificity

This paper presents a new clinical decision support system for diagnosing patients with Chronic Renal Failure (CRF) which is not yet thoroughly explored in literature. This paper aims at improving performance of a previously reported CRF diagnosis system which was based on Artificial Neural Network (ANN), Decision Tree (DT) and Naïve Bayes (NB) classifying algorithms. This is achieved by utilizing more efficient data mining classifiers, Support Vector Machine (SVM) and Logistic Regression (LR), in order to: (i) diagnose patients with CRF and (ii) determine the rate at which the disease is progressing. A clinical dataset of more than 100 instances is used in this study. Performance of the developed decision support system is assessed in terms of diagnostic accuracy, sensitivity, specificity and decisions made by consultant specialist physicians. The open source Waikato Environment for Knowledge Analysis library is used in this study to build and evaluate performance of the developed data mining classifiers. The obtained results showed SVM to be the most accurate (93.14%) when compared to LR as well as other classifiers reported in the previous study. A complete system prototype has been developed and tested successfully with the aid of NHS collaborators to support both diagnosis and long-term management of the disease.

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