Work

Predictors of early discharge in health facilities after birth in East African. a machine learning approach

Year: 2026

Type: software

Abstract: Code for the manuscript "Predictors of early discharge in health facilities after birth in East Africa: A machine learning approach" (under review at PLOS ONE). This repository contains Python code for data preprocessing, machine learning model training and tuning (LR, DT, RF, AdaBoost, XGBoost, KNN) using Optuna, SHAP interpretability, association rule mining (Apriori), and subgroup-stratified performance evaluation. Requires Python 3.10+ and libraries listed in requirements.txt. DHS data not included due to data use restrictions. See README.md for full details.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.21901813

Authors: Gonfa, Naol, Nenko Dube, Geleta, Wake, Fikadu, Gosha, Shuma, Sefera, Birbirsa, Alganeh, Habtamu, Hassen, Mohammedjud, Enyew, Ermias Bekele, Bekele, Zinabu

Institutions: Mattu University, Bule Hora University, Bahir Dar University, Wollo University, Samara National Research University, Samara University

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7202283285

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