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Hyper tuning hybrid MLP-CatBoost classifier using Mayfly optimization for detection of heart disease.

Diagnosing heart disease is considered a difficult task as it provides a digital estimation of the seriousness of the disease. As a result, the quickest treatment can be done. So, heart diagnosis has attracted a lot more attention in the medical industry throughout the globe, and along with excellen...

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Published in:Health Services & Outcomes Research Methodology Vol. 25; no. 3; pp. 305 - 328
Main Authors: MahaLakshmi, N. Venkata, Rout, Ranjeet Kumar
Format: equations & formulas pictorial research tables/charts Journal Article
Published: Springer Nature Sep2025
Online Access:View this record in EBSCOhost
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      jtl: Health Services & Outcomes Research Methodology
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      dt: Sep2025
      vid: 25
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      pub: Springer Nature
      place: New York, New York
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        187436323
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        10.1007/s10742-025-00341-0
        187436323
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        atl: Hyper tuning hybrid MLP-CatBoost classifier using Mayfly optimization for detection of heart disease.
      aug:
        au:
          MahaLakshmi, N. Venkata
          Rout, Ranjeet Kumar
        affil: https://ror.org/01vmfpj79 National Institute of Technology, Hazratbal, 190006, Srinagar, Jammu and Kashmir, India
      sug:
        subj:
          Heart Diseases Diagnosis
          Machine Learning
          Human
          Machine Learning Algorithms
          Descriptive Statistics
          Multilayer Perceptrons
          Decision Support Systems, Clinical
          Risk Assessment
      ab: Diagnosing heart disease is considered a difficult task as it provides a digital estimation of the seriousness of the disease. As a result, the quickest treatment can be done. So, heart diagnosis has attracted a lot more attention in the medical industry throughout the globe, and along with excellence in efficacy, the optimization algorithm plays a crucial role in the detection of heart disease. Here, to predict the cardiac illness an improvised CatBoost algorithm and Multi-layer Perceptron classifier are used. Also, proper hyperparameter tweaking is needed for the successful implementation of the classifier. In order to optimize the hybrid model's hyperparameters, the Mayfly optimization algorithm is deployed for effective hyperparameter optimization. In order to increase prediction accuracy, the Harris-Hawks optimization technique is used to choose the essential features from the dataset. Z-Alizadeh Sani and Cleveland heart disease datasets are utilized to detect heart disease. Also, it is compared with the existing models. To validate the efficiency of a model, six various measures are used: precision, accuracy, recall, the F- 1 measure, specificity, and loss. Here, when compared to the previous studies, the proposed model yields better performance, i.e., 98.7% accuracy with Cleveland and 99.2% with Alizadeh Sani Datasets.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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