Development of a machine learning model for prediction of uroflowmetry parameters and early diagnosis of lower urinary tract disorders.
| dc.contributor.author | Balayo, Ignatius | |
| dc.date.accessioned | 2026-10-15T07:07:10Z | |
| dc.date.available | 2026-10-15T07:07:10Z | |
| dc.date.issued | 2026 | |
| dc.description | Dissertation | |
| dc.description.abstract | This thesis develops and validates a machine learning approach for a non-invasive acoustic uroflowmetry system. The study serves as a proof of concept and lays the groundwork for a larger research project that will collect acoustic uroflowmetry data in Uganda. The research focused on three main objectives: (i) developing methods to process urinary sound recordings and comparing traditional machine learning with deep learning models for predicting urinary flow rate, (ii) creating a sequence-based model to reconstruct urinary flow curves and a classifier to support the early detection of lower urinary tract symptoms (LUTS), and (iii) evaluating all models using the same performance measures to ensure a fair comparison. The study used two clinically labeled datasets provided through a research collaboration with the University of Valladolid. The first dataset contained recordings from 50 patients collected simultaneously using a professional microphone, a smartphone, and a smartwatch. It was used to develop and compare flow-rate prediction models. The second dataset included 110 patients with confirmed clinical diagnoses and was used to build and evaluate the complete diagnostic model. For urinary flow-rate prediction, traditional machine learning models, including Random Forest, Support Vector Regression (SVR), and XGBoost, were compared with deep learning models, including a multilayer perceptron (MLP) and a convolutional neural network (CNN). The traditional models achieved the highest coefficients of determination, with 𝑅2 values ranging from 0.681 to 0.795, demonstrating strong agreement with the clinical measurements. However, the CNN achieved the lowest mean absolute errors, ranging from 2.43 to 2.69 mL/s. These findings suggest that neither approach consistently outperformed the other, as each offered different strengths depending on the evaluation metric. An important finding was that recordings captured using smartphones and smartwatches performed as well as, and in some cases better than, those recorded using the research-grade microphone. This demonstrates that reliable acoustic uroflowmetry can be achieved with affordable, widely available mobile devices and wearables. For diagnostic prediction, a deep learning model combining a convolutional encoder with a bidirectional long short-term memory (BiLSTM) decoder was developed to reconstruct urinary flow curves directly from acoustic recordings. Clinically relevant uroflowmetry parameters, including maximum flow rate (𝑄max), average flow rate (𝑄avg), voided volume (𝑉𝑉), and flow duration (𝑇flow), were extracted from the reconstructed curves and used to train a Random Forest classifier. Using five-fold stratified cross-validation, the classifier achieved an overall accuracy of 76.4% in distinguishing pathological from healthy voiding patterns. The model showed stronger performance in identifying pathological cases, achieving a recall of 0.809 compared with 0.620 for healthy cases. This behavior is appropriate for an initial screening tool, where reducing missed diagnoses is generally more important than maximizing specificity. Overall, this study demonstrates that it is technically feasible to build a complete, lowcost acoustic uroflowmetry pipeline, from audio recording and urinary flow estimation to diagnostic classification, using clinically labeled data. The findings show that consumer devices such as smartphones and smartwatches can provide reliable performance, making the technology more accessible for routine clinical use. The results also highlight that both 𝑅2 and mean absolute error should be considered when selecting predictive models, since each metric reflects a different aspect of performance. Although further clinical validation is required before the system can be deployed in routine healthcare, this work provides a strong foundation for the next phase of the research. Future work will focus on collecting a representative acoustic uroflowmetry dataset in Uganda, adapting the models to the local clinical environment, and improving access to affordable screening tools for lower urinary tract disorders in resource-constrained healthcare settings. | |
| dc.description.sponsorship | Dr. Odongtoo Godfrey ; Dr. Otim Timothy ; Busitema University | |
| dc.identifier.citation | Balayo, I. (2026). Development of a machine learning model for prediction of uroflowmetry parameters and early diagnosis of lower urinary tract disorders. [Unpublished dissertation]. Busitema University. | |
| dc.identifier.uri | https://bdears.busitema.ac.ug/handle/123456789/10957 | |
| dc.language.iso | en | |
| dc.publisher | Busitema University | |
| dc.title | Development of a machine learning model for prediction of uroflowmetry parameters and early diagnosis of lower urinary tract disorders. | |
| dc.type | Other |
Files
Original bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- Balayo_Ignatius_Final_Dissertation.pdf
- Size:
- 4.57 MB
- Format:
- Adobe Portable Document Format
License bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- license.txt
- Size:
- 1.71 KB
- Format:
- Item-specific license agreed to upon submission
- Description: