Metaheuristic-based hyperparameter optimization for multi-disease detection and diagnosis in machine learning

Metaheuristic algorithms with machine learning techniques have become popular because it works so well for problems like regression, classification, rule mining, and clustering in health care. This paper’s primary purpose is to design a multi-disease prediction system using AI-based metaheuristic ap...

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Vydané v:Service oriented computing and applications Ročník 18; číslo 2; s. 163 - 182
Hlavní autori: Singh, Jagandeep, Sandhu, Jasminder Kaur, Kumar, Yogesh
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Springer London 01.06.2024
Springer Nature B.V
Predmet:
Accuracy
Algorithms
Artificial intelligence
Artificial neural networks
Clustering
Computer Appl. in Administrative Data Processing
Computer Science
Computer Systems Organization and Communication Networks
Datasets
Decision trees
e-Commerce/e-business
Graphical representations
Health care
Health services
Heuristic methods
IT in Business
Machine learning
Management of Computing and Information Systems
Neural networks
Optimization
Searching
Software Engineering/Programming and Operating Systems
Special Issue Paper
Support vector machines
Medical data
Artificial neural network
Hyperparameters
Metaheuristics
Machine learning
Random search
ISSN:1863-2386, 1863-2394
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Popis
Shrnutí:Metaheuristic algorithms with machine learning techniques have become popular because it works so well for problems like regression, classification, rule mining, and clustering in health care. This paper’s primary purpose is to design a multi-disease prediction system using AI-based metaheuristic approaches. Initially, the data is collected in the form of diverse classes, which include Id, gender, date of birth, etc. The data has been preprocessed, normalized, and graphically represented to improve its quality and detect any errors. Later, machine learning models, such as decision tree, extra tree classifier, extreme gradient boosting classifier, light gradient boosting machine classifier, random forest, and artificial neural network, are initially trained without optimizing hyperparameters and then fine-tuned by integrating various hyperparameter optimizers such as grid search CV, random search, hyperband, and genetic search. During experimentation, it is found that optimizing the models using random search optimizer computed the highest accuracy of 100% as compared to the rest of the hyperparameter optimizers. In the context of ‘Service Oriented Computing and Applications,’ our multi-disease prediction system offers valuable innovation. It aligns with the goal of enhancing healthcare services, patient outcomes, and healthcare efficiency. Our pioneering integration of metaheuristic algorithms and machine learning introduces intelligent healthcare solutions, with the study’s focus on hyperparameter optimization and achieving 100% accuracy demonstrates practical significance in SOC and its applications.
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ISSN:1863-2386
1863-2394
DOI:10.1007/s11761-023-00382-8