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An efficient vector-borne disease prediction using SS optimization-based hybrid support vector random forest model

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Abstract

The transmission of infections caused by infected species or arthropoda, such as ticks, blackflies, sandflies, mosquitoes, and triatomine bugs, is known as vector-borne diseases. Arthropod vector is responsible in transmitting the most harmful illnesses that affect people as well as animals. This results in a significant impact on human health, leading to increased mortality rates and various side effects, ultimately reducing the life expectancy of people. This article proposes a novel approach to predict vector-borne diseases using medical data. The approach combines the Sine Cosine as well as Spotted Hyena-based Chimp Optimization Algorithm (SSC) as well as hybrid Support Vector Machine-based Random Forest (SVM-RF) approach. The SSC algorithm is designed by incorporating three different algorithms, namely the Chimp Optimization Algorithm, the Spotted Hyena Optimizer algorithm, and the Sine Cosine Algorithm (SCA). The proposed hybrid SVM-RF classifier approach accurately detects vector-borne diseases. Using the vector-borne dataset, the proposed SSC-optimized hybrid SVM-RF approach outperformed other approaches with values of 92, 93.25, 92.53, and 91.52%, respectively. Overall, the proposed approach has significant potential in predicting and diagnosing vector-borne diseases, which can ultimately lead to improved public health outcomes.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Information Technology, Rajalakshmi Engineering College, Chennai, India

    Babu Munirathinam

  2. Department of Networking and Communications, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India

    Vijay Vasanth Aroulanandam & Prabakeran Saravanan

Authors
  1. Babu Munirathinam
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  2. Vijay Vasanth Aroulanandam
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  3. Prabakeran Saravanan
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Contributions

All agreed on the content of the study. MB, VA and PS collected all the data for analysis. MB, VA and PS agreed on the methodology. MB, VA and PS completed the analysis based on agreed steps. Results and conclusions are discussed and written together. All authors read and approved the final manuscript.

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Correspondence to Babu Munirathinam.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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Munirathinam, B., Aroulanandam, V.V. & Saravanan, P. An efficient vector-borne disease prediction using SS optimization-based hybrid support vector random forest model. SIViP 17, 3943–3952 (2023). https://doi.org/10.1007/s11760-023-02624-w

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  • DOI: https://doi.org/10.1007/s11760-023-02624-w

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