Clinical pathway variance prediction using artificial neural network for acute decompensated heart failure clinical pathway


  • Muhammad Haikal Satria Universiti Teknologi Malaysia
  • Mohd Soperi Mohd Zahid Universiti Teknologi Malaysia
  • Mohamad Shukor Talib Universiti Teknologi Malaysia
  • Habibollah Haron Universiti Teknologi Malaysia
  • Azmee Abd Ghazi Institut Jantung Negara



Artificial Neural Network, Acute Decompensated Heart Failure, Variance Prediction, Clinical Pathway


Patients in modern healthcare demand superior healthcare quality. Clinical pathways are introduced as the main tools to manage this quality. A clinical pathway is a task-oriented care plan that specifies steps to be taken for patient care. It follows the clinical course according to the specific clinical problem. During clinical pathway execution, variance or deviation from the specified care plan could occur, and may endanger the patient’s life. In this paper, a proposed framework for artificial neural networks (ANNs) in clinical pathway variance predictions is presented. This proposed research method predicts the variance that may occur during Acute Decompensated Heart Failure Clinical Pathway. By using the Artificial Neural Network, 3 variances (Dialysis, PCI, and Cardiac Catherization) are predicted from 55 input. The results show that artificial neural networks with the Levenberg-Marquadt training algorithm with a 55-27-27-1 architecture achieve the best prediction rate, with an average prediction accuracy of 87.4425% for the training dataset and 85.255% for the test dataset.

Author Biographies

Muhammad Haikal Satria, Universiti Teknologi Malaysia

Faculty of Bioscience and Medical Engineering

Mohd Soperi Mohd Zahid, Universiti Teknologi Malaysia

Faculty of Bioscience and Medical Engineering

Mohamad Shukor Talib, Universiti Teknologi Malaysia

Faculty of Bioscience and Medical Engineering

Habibollah Haron, Universiti Teknologi Malaysia

Faculty of Bioscience and Medical Engineering

Azmee Abd Ghazi, Institut Jantung Negara

Department of Cardiology


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