Dennis Muriithi

A Transparent AI-Driven Ensemble Learning Approach for Predicting Mental Health Risks among University Students in Kenya

Victor Lumumba, Dennis Muriithi, Monica Oundo

This study addresses the escalating mental health crisis among university students in Kenya, where psychological distress, driven by academic, financial, social, and transitional pressures, is increasingly prevalent yet under-prioritized in public health discourse. While global studies report distress rates exceeding 75% and …

Kenya (code pays fourni par la source)

Early Detection of Hypertension Using Explainable AI Model: Random Forest Model

Elizabeth Wahito Waburi, Dennis Muriithi, Eugine Mukhwana Sundays

Hypertension is a leading cause of mortality worldwide. Early diagnosis and effective risk stratification are essential to reduce the prevalence of hypertension. The primary aim was to develop and evaluate a robust predictive model for assessing the individual risk of a binary …

Kenya (code pays fourni par la source)

Factors associated with psychological distress in mothers and girls in Kenya: A baseline analysis

Monicah Nyambura, Catherine Kathure Kaimenyi, Dennis Muriithi, Thomas Gordon et autres

BACKGROUND: Psychological distress may be an indicator of mental health, can lead to reduced life functioning, and is not well studied in African countries. OBJECTIVE: The aim of this analysis was to quantify psychological distress using the Kessler-6 (K-6) scale, which is …

An Explainable AI Framework for Neonatal Mortality Risk Prediction in Kenya: Enhancing Clinical Decisions with Machine Learning

Victor Lumumba, Dennis Muriithi, Elizabeth Wambui Njoroge, Amos Kipkorir Langat et autres

Neonatal mortality remains a critical public health challenge in Kenya, with a rate of 21 per 1,000 live births—well above the SDG 3.2 target. While machine learning (ML) offers potential for risk prediction, most models lack transparency and clinical interpretability, limiting their …

Kenya, Ouganda (code pays fourni par la source)

Integrating Explainable Machine Learning Models for Early Detection of Hypertension: A Transparent Approach to AI-Driven Healthcare

Elizabeth Waburi, Dennis Muriithi, Mukhwana Eugine Sundays

Hypertension is a major public health challenge globally, often undiagnosed until severe complications arise, highlighting the critical need for early and accurate risk prediction methods. Despite advances in machine learning (ML), many models remain black boxes, limiting clinical trust and adoption. This …

Kenya (code pays fourni par la source)

Evaluating the Performance of Selected Single Classifiers with Incorporated Explainable Artificial Intelligence (XAI) in the Prediction of Mental Health Distress Among University Students

Victor Lumumba, Dennis Muriithi, Monicah Buyatsi Oundo

The mental health of university students has emerged as a critical global public health concern, with increasing prevalence of anxiety, depression, and stress-related conditions reported across diverse academic environments. With the increasing mental health issues among university students all over the world, …

Kenya (code pays fourni par la source)

Explainable Artificial Intelligence Models for Predicting Malaria Risk in Kenya

Dennis Muriithi, Victor Lumumba, Olushina Olawale Awe, Daniel Mwangi Muriithi

The article aims to develop interpretable Machine Learning models using R statistical programming language for malaria risk prediction in Kenya, emphasizing leveraging Explainable AI (XAI) techniques to support targeted interventions and improve early detection mechanisms. The methodology involved using synthetic data with …

Kenya, br (code pays fourni par la source)

A Machine Learning-Based Prediction of Malaria Occurrence in Kenya

Dennis Muriithi, Victor Lumumba, Mark O. Okongo

For many years’ malaria has been a health public concern in Kenya as well as many parts of Africa and other parts of the world. The purpose of this study is to develop and evaluate a supervised machine learning model to predict …

Kenya (code pays fourni par la source)

A Machine Learning Approach for Prediction of Surgical Outcomes in Elective Surgery

Dennis Muriithi, Virginia Mwangi

The aim of this research was to design a Machine Learning (ML) approaches to predict surgical outcome associated with perioperative risks factors among patients undergoing elective surgery. The research employed descriptive cross-sectional survey and a sample size of 292 patients. Only adult …

Kenya (code pays fourni par la source)

Mathematical analysis of a comprehensive HIV AIDS model: treatment versus vaccination

Mark O. Okongo, J. Kirimi, A. L. Murwayi, Dennis Muriithi

Copyright c © 2013 M. O. Okongo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A comprehensive deterministic …

Kenya (code pays fourni par la source)