Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology Journal of Technology and Informatics (JoTI)
Ntumba Nkongolo, Albert
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
ARTIFICIAL LEARNING BASED ON KERNEL SVM FOR THE PREDICTION OF CARDIOVASCULAR DISEASE HYPERTENSION MUSUBAO SWAMBI, Patient; Ntumba Nkongolo, Albert; Kafunda Katalay, Pierre; Mabela Matendo Makengo, Rostin; Mbuyi Mukendi, Eugène
Jurnal Techno Nusa Mandiri Vol. 22 No. 1 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v22i1.6011

Abstract

Hypertension, a critical risk factor for cardiovascular diseases, requires accurate early detection for effective management. This study examines the application of kernel-based Support Vector Machines (SVM) for predicting hypertension, utilizing advanced machine learning techniques to address the complex, non-linear relationships inherent in healthcare data. By employing various kernel functions, such as the radial basis function (RBF) and polynomial kernels, the study aims to enhance the model's ability to capture and interpret the nuanced patterns associated with hypertension risk. The research utilizes a diverse dataset that includes demographic, physiological, and lifestyle variables, applying kernel SVM to predict hypertension outcomes. Performance is evaluated through rigorous cross-validation, with metrics including accuracy, precision, recall, and F1-score. The findings indicate that kernel SVMs significantly outperform traditional linear models, offering superior prediction accuracy and robustness. This study highlights the potential of advanced machine learning methods in improving early detection and personalized risk assessment for hypertension, ultimately supporting more effective management strategies and better cardiovascular health outcomes.
Semantic Knowledge Fusion in Healthcare: A Hybrid Approach for Connected Medicine Muhala Luhepa, Blaise; Bukasa Kakamba, John; Munduku Munduku, Deo; Mazono Magubu, Daniel; Ntumba Nkongolo, Albert; Matondo Mananga, Herman; Munene Asidi, Djonive
Journal of Technology and Informatics (JoTI) Vol. 7 No. 2 (2025): Vol. 7 N. 2 (2025)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v7i2.1182

Abstract

In a context where connected medicine requires increasingly explainable, accurate, and responsive systems, this paper presents an applied experimental research focusing on the development and evaluation of a hybrid intelligent assistant for healthcare data fusion. The study is based on the parallel combination of two data paradigms: classical tabular structures and their ontological equivalent. Using an intelligent assistant, we simultaneously query a medical dataset on diabetes in tabular form and the same dataset translated into an OWL ontology that can be queried using SPARQL. The aim is to demonstrate that the synchronised combination of these two models not only provides a more complete response but also one that is better contextualised and clinically exploitable. The research follows an experimental methodology, involving the implementation, testing, and comparative evaluation of both models on 300 questions classified by increasing complexity (simple, complex, and very complex). The results reveal a relevance rate above 99%, with an average response time suited to medical use. This work highlights the potential of hybrid architectures in connected health and paves the way for new decision-making assistants that fully exploit the semantic richness of medical knowledge.
Co-Authors Bukasa Kakamba, John Kafunda Katalay, Pierre Mabela Matendo Makengo, Rostin MATONDO MANANGA, Herman Mazono Magubu, Daniel Mbuyi Mukendi, Eugène Muhala Luhepa, Blaise Munduku Munduku, Deo Munene Asidi, Djonive MUSUBAO SWAMBI, Patient