Medicine and Health Sciences Commons^™

Performance Of Machine Learning Classifiers In Classifying Stunting Among Under-Five Children In Zambia, Obvious Nchimunya Chilyabanyama, Roma Chilengi, Roma Chilengi, Michelo Simuyandi, Caroline C. Chisenga, Masuzyo Chirwa, Kalongo Hamusonde, Rakesh Kumar Saroj, Najeeha Talat Iqbal, Innocent Ngaruye

Department of Paediatrics and Child Health

Stunting is a global public health issue. We sought to train and evaluate machine learning (ML) classification algorithms on the Zambia Demographic Health Survey (ZDHS) dataset to predict stunting among children under the age of five in Zambia. We applied Logistic regression (LR), Random Forest (RF), SV classification (SVC), XG Boost (XgB) and Naïve Bayes (NB) algorithms to predict the probability of stunting among children under five years of age, on the 2018 ZDHS dataset. We calibrated predicted probabilities and plotted the calibration curves to compare model performance. We computed accuracy, recall, precision and F1 for each machine learning algorithm. …

Nonlinear Association Of Nurse Staffing And Readmissions Uncovered In Machine Learning Analysis, Olga Yakusheva, James Bang, Ronda G. Hughes, Kathleen L. Bobay, Linda L. Costa, Marianne Weiss

College of Nursing Faculty Research and Publications

Objective: Several studies of nurse staffing and patient outcomes found a curvilinear or U-shaped relationship, with benefits from additional nurse staffing diminishing or reversing at high staffing levels. This study examined potential diminishing returns to nurse staffing and the existence of a "tipping point" or the level of staffing after which higher nurse staffing no longer improves and may worsen readmissions.

Data Sources/Study Setting: The Readiness Evaluation And Discharge Interventions (READI) study database of over 130,000 adult (18+) inpatient discharges from 62 medical, surgical, and medical-surgical (noncritical care) units from 31 United States (US) hospitals during October 2014-March 2017.

Study …