PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Aging is associated with a progressive decline in cognitive functions, driving the development of digital interventions to mitigate its impact. This study evaluated a web-based application designed to enhance cognitive performance in older adults through a Multilayer Perceptron (MLP) model optimized using K-fold cross-validation (K=5). A total of 100 participants aged 65 and older were randomly assigned to an experimental group and a control group. Over 16 weeks, the experimental group used the personalized application, while the control group accessed non-adaptive content. Statistical analysis revealed a significant improvement in the experimental group, with an average cognitive score increase of 37% (95% CI: 8.8–9.8), compared to 10% in the control group (95% CI: 5.5–6.3). The model achieved an accuracy of 89% and an area under the curve (AUC) of 0.93, demonstrating its ability to predict cognitive improvements effectively. Additionally, 92% of participants completed more than 80% of the sessions, indicating high adherence. Usability evaluation reported an average score of 4.7/5, reflecting positive perceptions regarding the platform’s accessibility and usefulness. These findings support the integration of machine learning techniques into cognitive stimulation programs, highlighting their potential for incorporation into digital healthcare systems to improve the quality of life in aging populations. Future research could explore deep learning models and dimensionality reduction techniques to further optimize intervention personalization.