An intelligent disease prediction system for psychological diseases by implementing hybrid hopfield recurrent neural network approach
Machine learning based approaches for automatic disease prediction is a novel research area in healthcare informatics. Electronic Health Records in medical settings improves early-stage illness diagnosis. However, when standard rule-based approaches, like doctor's prescription or laboratory tes...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-05-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305323000339 |
| _version_ | 1797804556490375168 |
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| author | Vikas Kamra Praveen Kumar Masoud Mohammadian |
| author_facet | Vikas Kamra Praveen Kumar Masoud Mohammadian |
| author_sort | Vikas Kamra |
| collection | DOAJ |
| description | Machine learning based approaches for automatic disease prediction is a novel research area in healthcare informatics. Electronic Health Records in medical settings improves early-stage illness diagnosis. However, when standard rule-based approaches, like doctor's prescription or laboratory test reports are employed for disease diagnosis, the advantages of EHRs are not accomplished adequately. As a result, there is a requirement of technology based solution which helps in prediction of psychological diseases in a more efficient way. The proposed research work offers a hybrid Hopfield recurrent neural network (H2RN2) approach to predict psychological diseases by using amorphous clinical EHRs taken from Kaggle database. The proposed model automatically learns inherent semantic characteristics from available clinical data items. It uses fivefold cross validation technique within a recurrent neural network which detracts over fitting of the model. In addition to effective learning during training of the model, the hybrid approach also helps in accurate prediction of the disease with improved accuracy. The proposed model is assessed using three measuring parameters, accuracy, recall and F1-score and yields an accuracy of 97.53% in experimental evaluation, which is superior to several existing approaches for psychological disease prediction. The results demonstrate that the proposed model outperforms several other techniques in predicting the risk of psychiatric disorders. In future, the similar approach may be employed to predict gender-based psychological diseases or to anticipate the risk of various physiological diseases. |
| first_indexed | 2024-03-13T05:38:58Z |
| format | Article |
| id | doaj.art-cf7ecc17a51f46ed97c2a73be1a40dd0 |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-03-13T05:38:58Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-cf7ecc17a51f46ed97c2a73be1a40dd02023-06-14T04:34:50ZengElsevierIntelligent Systems with Applications2667-30532023-05-0118200208An intelligent disease prediction system for psychological diseases by implementing hybrid hopfield recurrent neural network approachVikas Kamra0Praveen Kumar1Masoud Mohammadian2Research Scholar, Amity University Uttar Pradesh, Sector-125, Noida, Uttar Pradesh 201313, India; Corresponding author at: Research Scholar, CSE Department, ASET, Amity University Uttar Pradesh.CSE Department, Amity University Uttar Pradesh, Noida, IndiaFaculty of Science and Technology, University of Canberra, Australia, ACT 2601Machine learning based approaches for automatic disease prediction is a novel research area in healthcare informatics. Electronic Health Records in medical settings improves early-stage illness diagnosis. However, when standard rule-based approaches, like doctor's prescription or laboratory test reports are employed for disease diagnosis, the advantages of EHRs are not accomplished adequately. As a result, there is a requirement of technology based solution which helps in prediction of psychological diseases in a more efficient way. The proposed research work offers a hybrid Hopfield recurrent neural network (H2RN2) approach to predict psychological diseases by using amorphous clinical EHRs taken from Kaggle database. The proposed model automatically learns inherent semantic characteristics from available clinical data items. It uses fivefold cross validation technique within a recurrent neural network which detracts over fitting of the model. In addition to effective learning during training of the model, the hybrid approach also helps in accurate prediction of the disease with improved accuracy. The proposed model is assessed using three measuring parameters, accuracy, recall and F1-score and yields an accuracy of 97.53% in experimental evaluation, which is superior to several existing approaches for psychological disease prediction. The results demonstrate that the proposed model outperforms several other techniques in predicting the risk of psychiatric disorders. In future, the similar approach may be employed to predict gender-based psychological diseases or to anticipate the risk of various physiological diseases.http://www.sciencedirect.com/science/article/pii/S2667305323000339Psychological disordersIntelligent systemsNatural language processingDecision support systemHopfield networkRecurrent Neural Network |
| spellingShingle | Vikas Kamra Praveen Kumar Masoud Mohammadian An intelligent disease prediction system for psychological diseases by implementing hybrid hopfield recurrent neural network approach Intelligent Systems with Applications Psychological disorders Intelligent systems Natural language processing Decision support system Hopfield network Recurrent Neural Network |
| title | An intelligent disease prediction system for psychological diseases by implementing hybrid hopfield recurrent neural network approach |
| title_full | An intelligent disease prediction system for psychological diseases by implementing hybrid hopfield recurrent neural network approach |
| title_fullStr | An intelligent disease prediction system for psychological diseases by implementing hybrid hopfield recurrent neural network approach |
| title_full_unstemmed | An intelligent disease prediction system for psychological diseases by implementing hybrid hopfield recurrent neural network approach |
| title_short | An intelligent disease prediction system for psychological diseases by implementing hybrid hopfield recurrent neural network approach |
| title_sort | intelligent disease prediction system for psychological diseases by implementing hybrid hopfield recurrent neural network approach |
| topic | Psychological disorders Intelligent systems Natural language processing Decision support system Hopfield network Recurrent Neural Network |
| url | http://www.sciencedirect.com/science/article/pii/S2667305323000339 |
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