Hybrid Firefly and Grey Wolf Optimization for Enhanced SVM-Based Chronic Kidney Disease Detection

Problem Definition

Research in the field of detecting and diagnosing Chronic Kidney Disease (CKD) has highlighted the importance of utilizing classification and neural networks, with Support Vector Machine (SVM) emerging as an effective classifier. However, limitations arise in the reliance on manually setting parameters such as box constraints and sigma values, which are crucial for SVM performance. The need for adjusting these parameters based on different datasets adds complexity to the model and hinders its dynamic adaptability. Moreover, the static behavior of the classifier for specific datasets further underscores the necessity for developing a more reliable and dynamic model. The current challenges faced within the domain of CKD detection underscore the urgency for an innovative solution that can overcome these limitations and enhance the diagnostic accuracy and efficiency of the classification process.

Objective

The objective of this research is to develop a dynamic and reliable model for predicting Chronic Kidney Disease (CKD) using Support Vector Machine (SVM) classifier, while addressing the limitations of manual parameter adjustment and static behavior observed in existing models. By incorporating optimization algorithms such as Firefly Algorithm (FA) and Grey Wolf Optimizer (GWO), the aim is to optimize the performance of the SVM classifier and enhance the diagnostic accuracy and efficiency of the classification process for CKD detection.

Proposed Work

Research in the field of detecting and diagnosing Chronic Kidney Disease (CKD) has highlighted the importance of classification and neural networks, with the SVM classifier showing promising results. However, the static behavior of SVM for a specific dataset and the need for manual adjustment of parameters such as box constraint and sigma values has raised concerns regarding the complexity and adaptability of the model. To address this, the proposed work aims to develop a dynamic and reliable model for predicting CKD using SVM and optimize its performance by incorporating two optimization algorithms - Firefly Algorithm (FA) and Grey Wolf Optimizer (GWO). With the SVM classifier known for its effectiveness in CKD prediction due to its ability to measure the distance in a transformed function space using the Gaussian Kernel, implementing optimization algorithms such as FA and GWO can further enhance the model's performance. The selection of these algorithms was based on their ease of implementation and ability to provide highly effective solutions.

FA promotes data sharing among the population to improve search results, while GWO offers high search precision with a simple approach that requires no initial parameters. By integrating these algorithms with the SVM classifier, the proposed model aims to create a more dynamic and adaptable system for predicting CKD, thus addressing the limitations of manual parameter adjustment and static behavior observed in existing models.

Application Area for Industry

This project can be applied in various industrial sectors such as healthcare, pharmaceuticals, biotechnology, and research institutions. The proposed solutions for incorporating optimization techniques to enhance SVM classifier performance can address specific challenges these industries face in detecting and diagnosing Chronic Kidney Disease (CKD). By utilizing optimization algorithms such as Firefly Algorithm and Grey Wolf Optimization, the model can adapt dynamically to changes in the dataset, improving the accuracy and efficiency of the classification process. The benefits of implementing these solutions include ease of implementation, highly effective results, and improved searching precision, which can ultimately lead to better diagnostic outcomes and more reliable models in the field of CKD detection.

Application Area for Academics

The proposed project has the potential to enrich academic research, education, and training in the field of detecting and diagnosing Chronic Kidney Disease (CKD). By incorporating optimization techniques such as Firefly Algorithm (FA) and Grey Wolf Optimization (GWO) to enhance the performance of Support Vector Machine (SVM) classifier, the project offers a dynamic and reliable model for detecting CKD. This project is relevant in pursuing innovative research methods by improving the efficacy of the SVM classifier through optimization algorithms. Researchers, MTech students, and PhD scholars in the field of bioinformatics, medical informatics, and machine learning can benefit from this project by utilizing the code and literature for their work. Specifically, the project covers the technology of SVM, ANFIS, Soft computing algorithms (GWO, FA), and Infinite feature selection, offering a wide range of applications for data analysis and simulations in educational settings.

The dynamic nature of the model and the incorporation of optimization techniques address the challenges of adapting to changes in the dataset and improving the performance of the classifier. By utilizing FA and GWO, the project aims to provide high search precision and easy implementation, making it accessible for researchers and students to explore and apply in their research work. The future scope of this project includes further optimization techniques, integration of other algorithms, and application in different domains of medical diagnosis and disease detection. By continuing to explore and enhance the model, this project has the potential to contribute significantly to academic research and education in the field of CKD detection and diagnosis.

Algorithms Used

The project uses various algorithms such as ANFIS, SVM, Soft computing (GWO, FA), and Infinite feature selection to enhance the accuracy and efficiency of detecting Chronic Kidney Disease (CKD). SVM is chosen for its effectiveness in measuring the distance between molecules and hyperplanes using the kernel trick, particularly the Gaussian Kernel. To dynamically adapt the model to dataset changes, optimization techniques are incorporated with SVM, such as Particle Swarm Optimization, Ant Colony Optimization, BAT algorithm, Firefly algorithm, Grey Wolf Optimization, and Genetic Algorithm. Firefly algorithm and GWO are selected for their ease of implementation and ability to improve search results and precision without initial parameters. These algorithms play a crucial role in improving the efficacy of the solution for CKD detection.

Keywords

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SEO Tags

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