A Mobile-Based Machine-Learning Model for Predicting Maintenance of Dental Machines for Health Facilities: A Case of Ministry of Health in Uganda

Famina Ayebare (), Shubi Felix Kaijage (), Neema Mduma (), Liston Kiwoli (), Hillary Kaluuma (), Lambert Byarugaba () and Lydia Ssanyu ()
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Famina Ayebare: Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST)
Shubi Felix Kaijage: Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST)
Neema Mduma: Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST)
Liston Kiwoli: Computational and Communication Science and Engineering (CoCSE), The Nelson Mandela African Institution of Science and Technology (NM-AIST)
Hillary Kaluuma: Makerere University, Infectious Diseases Institute
Lambert Byarugaba: Makerere University, Infectious Diseases Institute
Lydia Ssanyu: Makerere University, Infectious Diseases Institute

A chapter in Advancement in Embedded and Mobile Systems, 2026, pp 93-103 from Springer

Abstract: Abstract The health sector remains underfunded, falling short of the 15% budget allocation recommended by the Abuja Declaration of which Uganda is a member. Between 2010 and 2016, the budget of the health sector was an average of 7.8% of the national budget. In 2020/2021, it accounted for 5.1% of the national budget, from the previous 7.9% in the previous financial year. Major issues facing the health system include grossly underpaid health practitioners, scarcity of health workers, and necessary equipment in government facilities. The concept of machine learning is ubiquitous, and different algorithms have been used to train and build models to come up with different solution in the health sector. The objective of this project was to come up with a mobile-based machine-learning model for predicting the maintenance of dental machines based on the failure type to determine whether the machine is in good condition to work on patients efficiently or needs repair and maintenance. The project used both quantitative and qualitative methods of data collection. Random Forest and XG boost classifiers were used to train and test the model using structured data. The dataset contained 8 features, which were the product ID, type of machine, UDI (unique identifier), air temperature, process temperature, rotational speed, torque, and tool wear. Heat dissipation, power failure, random failure, overstain, and no failure were labels used to predict the type of failure that could occur, XG boost classifier emerged as the best with accuracy score of 97.3% in comparison with Random Forest after the accuracy score and confusion matrix of the two algorithms.

Keywords: Mobile-based; Machine-learning model; Predictive maintenance; Failure type; Ministry of Health (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-031-99219-3_7

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