An Innovative Approach Based on Machine Learning to Evaluate the Risk Factors Importance in Diagnosing Keratoconus

Background and objective: Keratoconus is a non-inflammatory corneal condition affecting both eyes and is present in one out of every 2,000 people worldwide. The cornea deforms into a conical shape and thins, resulting in high-order aberrations and gradual vision loss. Risk factor analysis in the degradation of keratoconus is under-researched. Methods: This research work investigates and uses effective machine learning models to gain insight into how much the risk factors of a patient contribute towards the progressive stages of keratoconus, as well as how significant these factors are in the creation of an accurate prediction model. This research demonstrates the value of machine learning approaches on a clinical dataset. This research paper employs several machine learning algorithms to classify the patients' stage of keratoconus using clinical information, such as measurements of the cornea's topography, elevation, and pachymetry taken using pentacam equipment at Sydney's Vision Eye Institute Chatswood. Results: Eight different machine learning techniques were investigated over three variations of a dataset and achieved an average accuracy of 68, 80, and 90% for the risk factor, pentacam, and cumulative datasets, respectively. The results show a significant increase in accuracy and a 97% increase in AUC upon addition of risk factor data compared to the models trained on pentacam data alone. The machine learning methods shown in this paper outperform those in current research. Conclusions: This research highlights the importance of machine learning methods and risk factor data in the diagnosis of keratoconus and highlights the patient's primary optical aid as the strongest risk factor. The goal of this research is to support the work of the ophthalmologists in diagnosing keratoconus and provide better care for the patient

Enhanced surveillance for covid-19 response in Lagos State, Nigeria: lessons learnt, 2020

Background The SARS-CoV-2, the novel virus which causes the coronavirus disease (COVID-19), has changed the world. No aspect of humanity is untouched from health, aviation, service industry, politics, economy, education, and entertainment to social and personal lives, since the outbreak of influenza-like illness in Wuhan, China, in December 2019. The Lagos State COVID-19 response team deployed enhanced surveillance through Active Case Search (ACS) for Acute Respiratory Infections (ARI) at health facilities and communities in the 20 Local Government Areas (LGAs) of Lagos State. Lagos State was the first state in Nigeria to deploy this specific surveillance strategy for Nigeria’s COVID-19 response. Methods We utilized descriptive and quantitative approaches to describe and assess the impact of the Active Case Search (ACS) for Acute Respiratory Infections (ARI) in health facilities and communities in 20 LGAs of Lagos State between 1st April and 15th May 2020. Results We found a significant difference in mean scores of suspected COVID-19 cases (M=60, SD=109, before ACS for ARI compared to M=568, SD=732, after ACS for ARI, P=0.0039), confirmed cases (M=10, SD=19, before ACS for ARI compared to M=144, SD=187, after ACS for ARI, P=0.0028) and contacts (M=56, SD=116, before ACS for ARI compared to M=152, SD=177, after ACS for ARI, P=0.044) before and after ACS for ARI in 20 LGAs of Lagos State, between 1st April and 15th May 2020. Conclusion The deployment of the Lagos State government’s polio-eradication structure for the COVID-19 response is both innovative and effective. The response to COVID-19 requires robust surveillance, credible and timely communication, collaboration, coordination among government, inter-governmental organizations (e.g., WHO), non-governmental organizations, and citizens to succeed and limit the medical, economic, social, and personal losses to the COVID-19 pandemic