Enhancing SVM and KNN Performance Through Preprocessing Pipelines for Interactive mHealth Applications

Abstract: Mobile health (mHealth) applications are increasingly relying on artificial intelligence (AI) to provide accurate and real-time decision support for healthcare delivery. However, achieving the optimal balance between processing time and accuracy remains challenging, especially for interactive applications that rely on cloud computing for scalability and performance. This study investigates the impact of data preprocessing techniques on the performance of two widely used machine learning algorithms, Support Vector Machine (SVM) and k-Nearest Neighbors (KNN), in cloud-based mHealth systems. We evaluate the effects of various scaling methods and dimensionality reduction techniques, on processing time and model accuracy. Our results demonstrate that preprocessing significantly improves model performance, with SVM achieving a precision of 0.72 and a processing time of 0.087 ms using StandardScaler, while KNN demonstrates the fastest processing times when paired with robust preprocessing. These findings underscore the importance of optimizing both data preparation and algorithmic efficiency for interactive mHealth applications. By enhancing model accuracy and reducing latency, this research contributes to the development of cost-effective, real-time mobile health systems that improve user experience and decision-making in healthcare.

Btissam Elaziz, Charaf Eddine AIT ZAOUIAT, Mohamed Eddabbah and Yassin LAAZIZ. “Enhancing SVM and KNN Performance Through Preprocessing Pipelines for Interactive mHealth Applications”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.6 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160665

@article{Elaziz2025,
title = {Enhancing SVM and KNN Performance Through Preprocessing Pipelines for Interactive mHealth Applications},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160665},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160665},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Btissam Elaziz and Charaf Eddine AIT ZAOUIAT and Mohamed Eddabbah and Yassin LAAZIZ}
}