OKEY, GODWIN, IFEANYI (2026) DEVELOPMENT OF PROSTATE ENLARGEMENT RISK PREDICTION AND MANAGEMENT STRATEGIES AMONG ADULT MEN. Other thesis, Godfrey Okoye University, Enugu Nigeria.
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Okey Ifeanyi project.PDF Download (3MB) |
Abstract
The condition commonly known as prostate enlargement is also called Benign Prostatic Hyperplasia (BPH). It is among the most common urological conditions in the adult and older men populations all over the world. It is very important that an early detection of the risks of having BPH is conducted for proper handling of the risk and prevention of complications. However, the current methods for assessing risks are usually centered on consulting a physician and conducting other tests which may not be affordable to everybody. For developing the model, it became necessary to use the OOAD approach, which utilizes Python as the programming language on its back end while HTML, CSS, and Javascript are used as the front-end technologies. Also, guided questionnaires are developed to help in collecting data concerning various aspects such as age, PSA value, IPSS, BMI, nocturia, frequency of urine excretion, poor urine flow, diabetes, and other related factors. In addition, two machine learning algorithms, namely, Random Forest and Logistic Regression, are trained by applying historical datasets acquired from the clinic. Evaluation of the algorithm will involve applying techniques such as five-fold cross-validation as well as metrics including Accuracy, Precision, Recall, F1-Score, Balanced Accuracy, and AUC. A dataset containing 2,097 patient records was used for model training and validation. The developed system achieved an accuracy of 64.68%, precision of 66.08%, recall of 67.87%, F1-score of 66.96%, and AUC of 67.40%. The system provides risk classification, probability scores, management advice, and escalation recommendations. The study demonstrates the potential of machine learning-based digital health solutions for early screening and management of prostate enlargement.
| Item Type: | Thesis (Other) |
|---|---|
| Subjects: | Q Science > Q Science (General) |
| Divisions: | Faculty of Computing And Information Technology (FACIT) |
| Depositing User: | Cynthia Ugwuoti |
| Date Deposited: | 30 Jul 2026 13:24 |
| Last Modified: | 30 Jul 2026 13:24 |
| URI: | http://eprints.gouni.edu.ng/id/eprint/6092 |
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