Evolutionary placement decisions of a multidisciplinary panel using genetic chromodynamics

Purpose – The purpose of this paper is to verify whether an evolutionary model outperforms logistic regression in determining the institutional placement decisions made by a London social service department panel. Design/methodology/approach – Genetic chromodynamics models an algorithm within the Michigan evolutionary classifier. Hence multiple classification rules evolve simultaneously. The dataset as described by Xie et al. is used. Two‐thirds of randomly selected cases are for training and one third for testing. Indicator weights are set between 0 and 1. Findings – Of 275 placements, 40 per cent represent residential homes, 48 per cent nursing homes, 12 per cent nursing long‐stay and two hospital long‐stay. In ten runs, 89.18 per cent were correctly placed (range 81.6 to 97.7 per cent); 5.07 per cent wrongly placed (range 1.2 to 8.0 per cent) and 5.75 per cent unplaced (range 0.0 to 11.5 per cent). Changing the 0.99 weights to 0.90 and 0.80 placed 87.6 and 87.9 per cent correctly. Research limitations/implications – Data came from written records. Errors in transcription and placement could not be checked. Other facts, or the weights, may be influencing placement decisions. Practical implications – Xie et al. matched 78 per cent of 195 placements. The evolutionary model outperformed logistic regression both in placements evaluated (275/195) and accuracy (89/78 per cent). Therefore, it could be used as a first line management information tool, revealing whether guidelines are followed. Originality/value – The authors have developed and tested a computational model, which could be used to evaluate institutional placement decisions in the UK “market”. Further development and exploitation would facilitate greater understanding of the needs old people and the resources necessary for their appropriate management.