Ranking multiple intelligence of people with epilepsy

Abstract

A person’s intelligence can be enhanced through focus and regular practice. Identifying the order of improving the intelligence parameters of People with Epilepsy (PWE) can help them to have a better understanding of their intelligence, which in turn can improve their chances of being employed. Because of their varying backgrounds, PWE differ in terms of which of their intelligence parameters need to be improved. The purpose of this study is to rank the intelligence parameters of PWE which need to be improved based on the patients’ demographics and illness background in order to help PWE to reduce the present gap between them and other people. The Analytic Hierarchy Process (AHP) is used to evaluate the impact of demographics and to determine the weights of qualitative factors that affect the intelligence parameters of PWE. The integrated AHP and Data Envelopment Analysis (DEA) method is used to rank the intelligence parameters of PWE to determine the prioritized intelligence parameters to be improved. To rank the PWE’s intelligence parameters, enhanced Russell measure (ERM) and ERM super-efficiency models in DEA are first used where the desirable weights for each input and output of each Decision Making Unit (DMU) is determined individually. Then, a new model for ranking DMUs by calculating the interval efficiency with a common set of weights (CSW) in DEA is proposed that determines the lower and upper-bounds of the interval efficiency over a CSW. The eight intelligences or skills are musical, bodily/kinaesthetic, logical/mathematical, spatial, linguistic, interpersonal, intrapersonal and naturalist. The considered patients’ demographics are seizure type, age, onset age, marital status, ethnicity, educational level, employment status, and gender. This study was administered on a data base of 158 epilepsy patients collected at Neurology Department, Kuala Lumpur Hospital from May 2007 to March 2009. The Expert Choice is employed to perform the sensitivity analysis with respect to explaining how the demographic features influence the intelligence parameters as alternatives, in addition to supporting and verifying the outcomes of the AHP model. The General Algebraic Modelling System (GAMS) is then employed to carry out the ranking, which is a sophisticated modelling system for mathematical optimization. The effect of demographics on intelligence parameters of PWE are identified. The study reveals that demographic information of PWE is essential for the exploration of the potential abilities of PWE. Sensitivity analysis demonstrates the robustness of the AHP assessment process and the effects of demographics on the intelligence parameters of PWE. The proposed new interval efficiency ranking method with CSW evaluates the PWE’s intelligence parameters from the same point of view and gives an interval for the efficiency score that allows decision makers to make subsequent decisions more carefully considering uncertainty. Integration of AHP and new model for ranking DMUs in DEA determines the priority of eight intelligence parameter skills. The new approach of ranking intelligence can be used to enhance the employability of PWE as the intelligence to be improved can be prioritized.