DESIGN AND IMPLEMENTATION OF NATURE-INSPIRED ALGORITHMS FOR BIOMEDICAL APPLICATIONS

Abstract

Over the last few decades, the volume of data has become colossal. The process of attaining optimal solutions is progressively becoming more intricate due to the proliferation of data generation. The ability to process large volumes of data in a short period of time is facilitated by optimization algorithms. Intelligent metaheuristic algorithms have gained recognition for their ability to achieve optimal solutions in complex optimization problems, particularly when faced with multiple restrictions. Numerous unique algorithms are currently being researched to devise efficient methodologies for addressing these types of situations. These algorithm supports feature selection which aids to pick up appropriate and important features from the original feature space with the minimum redundancy and the highest discriminating capability. Now-a-days, algorithms are computationally intensive and time consuming. There is a need of an optimization technique to solve optimization problems that provide results timely as well as handle multidimensional datasets in the field of biomedical. Due to the presence of redundant features and the challenge of high dimensionality, the learning engine incurs a significant time cost, resulting in a decrease in the efficiency of the model. The utilization of application classification analysis is employed to enhance medical diagnostic decision-making processes and ultimately enhance the standard of care provided to the patients. Within the realm of biomedical applications, there exists a range of activities encompassing illness diagnosis and patient treatment. These activities involve the utilization of computer analysis to examine patient-related data, the application of clinical decision-making processes, the integration of medical informatics and the incorporation of artificial intelligence techniques. The biomedical application of disease diagnosis in the v healthcare system pertains to patients who are in a state that indicates the presence of a disease. The diagnostic procedure within the healthcare system is intricately connected to patients who have symptoms that are suggestive of a certain disease or condition. The expeditious identification and application of individualized pharmaceutical treatments have notably enhanced the overall well-being of patients, presenting prospective remedies for a multitude of ailments that affect the global populace. The proposed research targets to serve this unaddressed issue by using Nature Inspired Algorithms for dimensionality reduction on biomedical datasets. The nature-inspired algorithms tend to pick up features based on various feature selection approaches that tends to impact the target variable more effectively. The optimal features obtained by nature-inspired algorithms using various feature selection approaches greatly reduces the computational time and cost. The optimality of features could be evaluated against various performance measure parameters using machine learning classifiers. This can greatly contribute to diagnostic procedure by early diagnosis of the disease which can aid in timely dispense of treatment to the patient.