{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T03:54:32Z","timestamp":1788839672183,"version":"build-2803163510"},"reference-count":36,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T00:00:00Z","timestamp":1664323200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,9,28]]},"abstract":"<jats:p>With the advancements in data mining, wearables, and cloud computing, online disease diagnosis services have been widely employed in the e-healthcare environment and improved the quality of the services. The e-healthcare services help to reduce the death rate by the earlier identification of the diseases. Simultaneously, heart disease (HD) is a deadly disorder, and patient survival depends on early diagnosis of HD. Early HD diagnosis and categorization play a key role in the analysis of clinical data. In the context of e-healthcare, we provide a novel feature selection with hybrid deep learning-based heart disease detection and classification (FSHDL-HDDC) model. The two primary preprocessing processes of the FSHDL-HDDC approach are data normalisation and the replacement of missing values. The FSHDL-HDDC method also necessitates the development of a feature selection method based on the elite opposition-based squirrel searchalgorithm (EO-SSA) in order to determine the optimal subset of features. Moreover, an attention-based convolutional neural network (ACNN) with long short-term memory (LSTM), called (ACNN-LSTM) model, is utilized for the detection of HD by using medical data. An extensive experimental study is performed to ensure the improved classification performance of the FSHDL-HDDC technique. A detailed comparison study reported the betterment of the FSHDL-HDDC method on existing techniques interms of different performance measures. The suggested system, the FSHDL-HDDC, has reached its maximum level of accuracy, which is 0.9772.<\/jats:p>","DOI":"10.1155\/2022\/1167494","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T13:53:47Z","timestamp":1664373227000},"page":"1-12","source":"Crossref","is-referenced-by-count":27,"title":["A Novel Feature Selection with Hybrid Deep Learning Based Heart Disease Detection and Classification in the e-Healthcare Environment"],"prefix":"10.1155","volume":"2022","author":[{"given":"Dwarakanath","family":"B.","sequence":"first","affiliation":[{"name":"Department of Information Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Latha","family":"M.","sequence":"additional","affiliation":[{"name":"Department of Information Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3343-541X","authenticated-orcid":true,"given":"Annamalai","family":"R.","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jagadish S.","family":"Kallimani","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Machine Learning, M S Ramaiah Institute of Technology, Bangalore, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8042-9768","authenticated-orcid":true,"given":"Ranjan","family":"Walia","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Model Institute of Engineering and Technology, Jammu, Jammu and Kashmir, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5974-2648","authenticated-orcid":true,"given":"Birhanu","family":"Belete","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Science Engineering, Jimma Institute of Technology, Jimma, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2953495"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1166\/jctn.2020.8877"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2018.2834156"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2016.09.007"},{"issue":"6","key":"5","article-title":"Efficient solution to the waste management process using IOT for smart trash can","volume":"5","author":"S. 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