SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence
Abstract Artificial Intelligence techniques are being used to analyse vast amounts of medical data and assist in the accurate and early diagnosis of diseases. The common brain related diseases are faced by most of the people which affects the structure and function of the brain. Artificial neural ne...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-82838-1 |
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| author | G. Sathish Kumar E. Suganya S. Sountharrajan Balamurugan Balusamy Adil O. Khadidos Alaa O. Khadidos Shitharth Selvarajan |
| author_facet | G. Sathish Kumar E. Suganya S. Sountharrajan Balamurugan Balusamy Adil O. Khadidos Alaa O. Khadidos Shitharth Selvarajan |
| author_sort | G. Sathish Kumar |
| collection | DOAJ |
| description | Abstract Artificial Intelligence techniques are being used to analyse vast amounts of medical data and assist in the accurate and early diagnosis of diseases. The common brain related diseases are faced by most of the people which affects the structure and function of the brain. Artificial neural networks have been extensively used for disease prediction and diagnosis due to their ability to learn complex patterns and relationships from large datasets. However, there are some problems like over-fitting, under-fitting, vanishing gradient and increased elapsed time occurred in the course of data analysis and prediction which results in performance degradation of the model. Therefore, a complex structure perception is much essential by avoiding over-fitting and under-fitting. This empirical study presents a statistical reduction approach along with deep hyper optimization (SRADHO) technique for better feature selection and disease classification with reduced elapsed time. Deep hyper optimization combines deep learning models with hyperparameter tuning to automatically identify the most relevant features, optimizing model accuracy and reducing dimensionality. SRADHO is used to calibrate the weight, bias and select the optimal number of hyperparameters in the hidden layer using Bayesian optimization approach. Bayesian optimization uses a probabilistic model to efficiently search the hyperparameter space, identifying configurations that maximize model performance while minimizing the number of evaluations. Three benchmark datasets and the classifier models logistic regression, decision tree, random forest, K-nearest neighbour, support vector machine and Naïve Bayes are used for experimentation. The proposed SRADHO algorithm achieves 98.2% of accuracy, 97.2% of precision rate, 98.3% of recall rate and 98.1% of F1-Score value with 0.3% of error rate. The execution time for SRADHO algorithm is 12 s. |
| format | Article |
| id | doaj-art-c154a2f79fe947e9acbc15682de47d36 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c154a2f79fe947e9acbc15682de47d362025-01-12T12:19:23ZengNature PortfolioScientific Reports2045-23222025-01-0115112710.1038/s41598-024-82838-1SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligenceG. Sathish Kumar0E. Suganya1S. Sountharrajan2Balamurugan Balusamy3Adil O. Khadidos4Alaa O. Khadidos5Shitharth Selvarajan6Centre for Computational Imaging and Machine Vision, Department of Artificial Intelligence and Data Science, Sri Eshwar College of EngineeringDepartment of Information Technology, Sri Sivasubramaniya Nadar College of EngineeringDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa VidyapeethamDepartment of Computer Science and Engineering, Shiv Nadar University Delhi-NCR CampusDepartment of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz UniversityDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz UniversityDepartment of Computer Science, Kebri Dehar UniversityAbstract Artificial Intelligence techniques are being used to analyse vast amounts of medical data and assist in the accurate and early diagnosis of diseases. The common brain related diseases are faced by most of the people which affects the structure and function of the brain. Artificial neural networks have been extensively used for disease prediction and diagnosis due to their ability to learn complex patterns and relationships from large datasets. However, there are some problems like over-fitting, under-fitting, vanishing gradient and increased elapsed time occurred in the course of data analysis and prediction which results in performance degradation of the model. Therefore, a complex structure perception is much essential by avoiding over-fitting and under-fitting. This empirical study presents a statistical reduction approach along with deep hyper optimization (SRADHO) technique for better feature selection and disease classification with reduced elapsed time. Deep hyper optimization combines deep learning models with hyperparameter tuning to automatically identify the most relevant features, optimizing model accuracy and reducing dimensionality. SRADHO is used to calibrate the weight, bias and select the optimal number of hyperparameters in the hidden layer using Bayesian optimization approach. Bayesian optimization uses a probabilistic model to efficiently search the hyperparameter space, identifying configurations that maximize model performance while minimizing the number of evaluations. Three benchmark datasets and the classifier models logistic regression, decision tree, random forest, K-nearest neighbour, support vector machine and Naïve Bayes are used for experimentation. The proposed SRADHO algorithm achieves 98.2% of accuracy, 97.2% of precision rate, 98.3% of recall rate and 98.1% of F1-Score value with 0.3% of error rate. The execution time for SRADHO algorithm is 12 s.https://doi.org/10.1038/s41598-024-82838-1Bayesian optimizationDeep hyper optimizationFeature selectionSingular matrixStatistical reduction approach |
| spellingShingle | G. Sathish Kumar E. Suganya S. Sountharrajan Balamurugan Balusamy Adil O. Khadidos Alaa O. Khadidos Shitharth Selvarajan SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence Scientific Reports Bayesian optimization Deep hyper optimization Feature selection Singular matrix Statistical reduction approach |
| title | SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence |
| title_full | SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence |
| title_fullStr | SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence |
| title_full_unstemmed | SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence |
| title_short | SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence |
| title_sort | sradho statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence |
| topic | Bayesian optimization Deep hyper optimization Feature selection Singular matrix Statistical reduction approach |
| url | https://doi.org/10.1038/s41598-024-82838-1 |
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