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

SIGNAL IMAGE AND VIDEO PROCESSING 2023年第8期17卷 3943-3952页

作者： Munirathinam, Babu Aroulanandam, Vijay Vasanth Saravanan, Prabakeran Rajalakshmi Engn Coll Dept Informat Technol Chennai India SRM Inst Sci & Technol Fac Engn & Technol Dept Networking & Commun Kattankulathur 603203 Tamil Nadu India

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.

关键词： Vector-borne disease prediction hyena optimization algorithm Chimp optimization algorithm Random Forest Approach and Support Vector Machine

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