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MKELM: Mixed Kernel Extreme Learning Machine using BMDA optimization for web services based heart disease prediction in smart healthcare.
Sheeba, Adlin; Padmakala, S; Subasini, C A; Karuppiah, S P.
Afiliación
  • Sheeba A; Department of Computer Science and Engineering, St. Joseph's Institute of Technology, Chennai, India.
  • Padmakala S; Department of Computer Science and Engineering, St. Joseph's Institute of Technology, Chennai, India.
  • Subasini CA; Department of Computer Science and Engineering, St. Joseph's Institute of Technology, Chennai, India.
  • Karuppiah SP; Department of MBA, St. Joseph's College of Engineering, Chennai, India.
Comput Methods Biomech Biomed Engin ; 25(10): 1180-1194, 2022 Aug.
Article en En | MEDLINE | ID: mdl-35174762
In recent years, cardiovascular disease becomes a prominent source of death. The web services connect other medical equipments and the computers via internet for exchanging and combining the data in novel ways. The accurate prediction of heart disease is important to prevent cardiac patients prior to heart attack. The main drawback of heart disease is delay in identifying the disease in the early stage. This objective is obtained by using the machine learning method with rich healthcare information on heart diseases. In this paper, the smart healthcare method is proposed for the prediction of heart disease using Biogeography optimization algorithm and Mexican hat wavelet to enhance Dragonfly algorithm optimization with mixed kernel based extreme learning machine (BMDA-MKELM) approach. Here, data is gathered from the two devices such as sensor nodes as well as the electronic medical records. The android based design is utilized to gather the patient data and the reliable cloud-based scheme for the data storage. For further evaluation for the prediction of heart disease, data are gathered from cloud computing services. At last, BMDA-MKELM based prediction scheme is capable to classify cardiovascular diseases. In addition to this, the proposed prediction scheme is compared with another method with respect to measures such as accuracy, precision, specificity, and sensitivity. The experimental results depict that the proposed approach achieves better results for the prediction of heart disease when compared with other methods.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático / Cardiopatías Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Comput Methods Biomech Biomed Engin Asunto de la revista: ENGENHARIA BIOMEDICA / FISIOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: India Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático / Cardiopatías Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Comput Methods Biomech Biomed Engin Asunto de la revista: ENGENHARIA BIOMEDICA / FISIOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: India Pais de publicación: Reino Unido