Adoption of ICT4D frameworks to support screening for depression in Nigerian universities

Ojeme, Blessing Onuwa

Adoption of ICT4D frameworks to support screening for depression in Nigerian universities

Doctoral Thesis

2018

Publisher

University of Cape Town

Department

Department of Computer Science

Faculty

Faculty of Science

Abstract

Health is fundamental to development and access to healthcare is a major health and development issue particularly in developing countries where preventable diseases and premature deaths still inflict a high toll. In Nigeria, for instance, under-financing, inefficient allocation of limited medical resources has led to quantitative and qualitative deficiencies in depression identification, and to growing gaps in facility and equipment upkeep. The focus of the present study is Nigerian University students who are at higher risk of clinical depression than other populations. Besides high crime rate, acute unemployment, terrorism, extreme poverty and serial outbreak of diseases, which are everyday life situations that trigger depression for a large proportion of Nigerian population, Nigerian University students are faced with additional problems of poor living and academic conditions. These include constant problems of accommodation and overcrowded lecture halls caused by increasing population of students, recurrent disruptions of academic calendar, heavy cigarette smoking and high level of alcohol consumption. Effective prevention of medical condition and access to healthcare resources are important factors that affect peoples’ welfare and quality of life. Regular assessment for depression has been suggested as the first important steps to its early detection and prevention. Investigations revealed that, besides the peculiar shortage in mental health professionals in Nigeria, the near absence of modern diagnostic facilities has made the management of this potentially detrimental problem impossible. Given this national health problem, and that it would take some time before resources, especially human, can be mustered, calls have been made by several bodies that other viable means that take cognisance of the difficulties of assessing mental healthcare be sought. This study is an attempt at exploring opportunities to increase flexibility in depression prevention and detection processes. The study investigated the effectiveness of developing computer-based methodologies, derived from machine learning and human computer interaction techniques for guiding depression identification process in Nigerian universities. Probabilistic Bayesian networks was used to construct models from real depression datasets that included 1798 data instances, collected from the mental health unit of University of Benin Teaching Hospital (UBTH) and primary care centre in Nigeria. The models achieved high performance on standard metrics, including: 94.3% accuracy, 94.4% precision, 0.943 F-Measure, 0.150 RSME, 0.923 R and 92.2% ROC. The findings from the information gain and mutual information show high correlation between “depression” and “alcohol or other drug consumption”; “depression” and family support and availability of accommodation”, but low correlation between “depression” and “cigarette smoking”. The results also show high correlation between “depression” and a synergistic combination of “impaired function and alcohol and other drug consumption”. Following the User-Centered design approach, a desktop-based screening tool was developed for use by University academic staff, as a first step, for regular screening of staff and students for depression, and where necessary, schedule appointment with the appropriate mental health authority for further diagnosis. Though the interesting results from the heuristic evaluations illuminate the challenges involved, it demonstrates the significance and relevance of end-user factors in the process of designing computer-aided screening intervention, especially with respect to acceptance of the system for use in non-clinical environment. The findings presented in this doctoral study provide compelling evidence of the huge potential that the collaboration of machine learning and usability techniques has for complementing available resources in the management of depression among University population in Nigeria. It is hoped that, given the persistent challenges of depression, the findings will be part of the ongoing global research to encourage the adoption of ICT4D frameworks for the prevention of more serious cases by empowering other population for an early first-line depression screening.

Keywords

Computer Science

Reference:

Collections

PhD / Doctoral

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