Integrating enhanced sparse autoencoder-based artificial neural network technique and softmax regression for medical diagnosis

Sarah A. Ebiaredoh-Mienye, Ebenezer Esenogho, Theo G. Swart

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


In recent times, several machine learning models have been built to aid in the prediction of diverse diseases and to minimize diagnostic errors made by clinicians. However, since most medical datasets seem to be imbalanced, conventional machine learning algorithms tend to underperform when trained with such data, especially in the prediction of the minority class. To address this challenge and proffer a robust model for the prediction of diseases, this paper introduces an approach that comprises of feature learning and classification stages that integrate an enhanced sparse autoencoder (SAE) and Softmax regression, respectively. In the SAE network, sparsity is achieved by penalizing the weights of the network, unlike conventional SAEs that penalize the activations within the hidden layers. For the classification task, the Softmax classifier is further optimized to achieve excellent performance. Hence, the proposed approach has the advantage of effective feature learning and robust classification performance. When employed for the prediction of three diseases, the proposed method obtained test accuracies of 98%, 97%, and 91% for chronic kidney disease, cervical cancer, and heart disease, respectively, which shows superior performance compared to other machine learning algorithms. The proposed approach also achieves comparable performance with other methods available in the recent literature.

Original languageEnglish
Article number1963
Pages (from-to)1-13
Number of pages13
JournalElectronics (Switzerland)
Issue number11
Publication statusPublished - Nov 2020


  • Artificial neural network
  • E-health
  • Machine learning
  • Medical diagnosis
  • Softmax regression
  • Sparse autoencoder
  • Unsupervised learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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