Advancements and Challenges in Geospatial Artificial Intelligence, Evaluating Support Vector Machines Models for Dengue Fever Prediction: A Structured Literature Review

This review examines recent advancements and ongoing challenges in applying Support Vector Machines within Geospatial Artificial Intelligence, specifically for dengue fever prediction. Recent developments in Support Vector Machines include the introduction of advanced kernel methods, such as Radial Basis Function and polynomial kernels, which enhance the model’s ability to handle complex spatial data and interactions. Integration with high-resolution geospatial data and real-time analytics has significantly improved predictive accuracy, particularly in mapping environmental factors influencing disease spread. However, challenges persist, including issues with data quality, computational demands, and model interpretability. Data scarcity and the high computational cost of Support Vector Machines, especially with non-linear kernels, necessitate optimization techniques and advanced computing resources. Parameter tuning and enhancing model interpretability are critical for effective implementation. Future research should focus on developing new kernels and hybrid models that combine Support Vector Machines with other machine learning approaches to address these challenges. Practical applications in public health can benefit from improved real-time data processing and high-resolution analytics, while ensuring adherence to ethical and regulatory standards. This review underscores the potential of Support Vector Machines in Geospatial Artificial Intelligence for disease prediction and highlights areas where further innovation and research are needed to enhance its practical utility in public health.

@article{Meileni2024,
  title     = {Advancements and Challenges in Geospatial Artificial Intelligence, Evaluating Support Vector Machines Models for Dengue Fever Prediction: A Structured Literature Review},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Hetty Meileni and Ermatita and Abdiansah and Nyayu Latifah Husni},
  doi       = {10.14569/IJACSA.2024.0150965},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150965}
}