{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:53:49Z","timestamp":1774540429791,"version":"3.50.1"},"reference-count":37,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,6,7]]},"abstract":"<jats:p>In the medical field, prediction accuracy over multi-diseases is significant and it is helpful for improving the patient\u2019s health. Most of the conventional machine learning techniques concentrates only on detecting single diseases. Only a few systems are developed for predicting more than one disease. The classification of multi-label data is a challenging issue. Patients have symptoms of various diseases while analyzing the medical data and hence it is necessary to implement tools for the earlier identification of problems. The patterns in the health data have been effectively identified through deep learning-based health risk prediction models. Thus, an efficient prediction model for predicting various types of diseases is implemented in this work. Initially, the required data regarding various types of diseases will be gathered from Kaggle database. The garnered healthcare data are pre-processed for quality enhancement. The pre-processing procedures include data cleaning, data transformation, and outlier detection are performed at first. The outlier detection is done using the \u201cDensity-Based Spatial Clustering of Applications with Noise (DBSCAN)\u201d approach. The pre-processed data is then given to the Weighted Convolutional Neural Network Feature with Dilated Gated Recurrent Unit (WCNNF-DGRU) model. Here, the pre-processed data is provided to the CNN structure for feature extraction, in which the weights are optimized by means of the Enhanced Kookaburra Optimization Algorithm (EKOA). Then the features from the weighted CNN layer are provided to the Dilated GRU structure to determine the final prediction output. Experimental verification is carried out on the implemented WCDG in predicting multiple diseases by comparing it with other conventional prediction models and optimization algorithms.<\/jats:p>","DOI":"10.3233\/idt-240368","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T15:57:13Z","timestamp":1718726233000},"page":"769-798","source":"Crossref","is-referenced-by-count":4,"title":["An efficient multi-disease prediction model using advanced optimization aided weighted convolutional neural network with dilated gated recurrent unit"],"prefix":"10.1177","volume":"18","author":[{"given":"Naveen Reddy","family":"M","sequence":"first","affiliation":[{"name":"Electronics and Communication Engineering, Anurag University, Hyderabad, Telangana, India"}]},{"given":"S.","family":"Satheeskumaran","sequence":"additional","affiliation":[{"name":"Research and Development, Anurag University, Hyderabad, Telangana, India"}]}],"member":"179","reference":[{"key":"10.3233\/IDT-240368_ref1","doi-asserted-by":"crossref","first-page":"121574","DOI":"10.1109\/ACCESS.2023.3328909","article-title":"A robust heart disease prediction system using hybrid deep neural networks","volume":"11","author":"Reshan","year":"2023","journal-title":"IEEE Access"},{"issue":"3","key":"10.3233\/IDT-240368_ref2","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1109\/TII.2021.3098306","article-title":"A stable AI-based binary and multiple class heart disease prediction model for IoMT","volume":"18","author":"Yuan","year":"2022","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"8","key":"10.3233\/IDT-240368_ref3","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1109\/JBHI.2019.2962366","article-title":"Long short-term memory recurrent neural networks for multiple diseases risk prediction by leveraging longitudinal medical records","volume":"24","author":"Wang","year":"2020","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.3233\/IDT-240368_ref4","doi-asserted-by":"crossref","first-page":"138210","DOI":"10.1109\/ACCESS.2020.3011374","article-title":"Prediction of chronic diseases with multi-label neural network","volume":"8","author":"Ge","year":"2020","journal-title":"IEEE Access"},{"issue":"3","key":"10.3233\/IDT-240368_ref5","first-page":"3682","article-title":"Multiple disease prediction using Machine learning algorithms","volume":"80","author":"Arumugam","year":"2023","journal-title":"Materials Today: Proceedings"},{"key":"10.3233\/IDT-240368_ref6","doi-asserted-by":"crossref","unstructured":"Karthikeyan H, Menakadevi T. 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