Open Access

ARTICLE

Optimized Predictive Framework for Healthcare Through Deep Learning

Yasir Shahzad^1,*, Huma Javed¹, Haleem Farman², Jamil Ahmad², Bilal Jan³, Abdelmohsen A. Nassani⁴

1 Department of Computer Science, University of Peshawar, Peshawar, 25120, Pakistan
2 Department of Computer Science, Islamia College Peshawar, Peshawar, 25120, Pakistan
3 Department of Computer Science, FATA University, Kohat, 26100, Pakistan
4 Department of Management, College of Business Administration, King Saud University, Riyadh, Saudi Arabia

* Corresponding Author: Yasir Shahzad. Email:

Computers, Materials & Continua 2021, 67(2), 2463-2480. https://doi.org/10.32604/cmc.2021.014904

Received 26 October 2020; Accepted 03 December 2020; Issue published 05 February 2021

Abstract

Smart healthcare integrates an advanced wave of information technology using smart devices to collect health-related medical science data. Such data usually exist in unstructured, noisy, incomplete, and heterogeneous forms. Annotating these limitations remains an open challenge in deep learning to classify health conditions. In this paper, a long short-term memory (LSTM) based health condition prediction framework is proposed to rectify imbalanced and noisy data and transform it into a useful form to predict accurate health conditions. The imbalanced and scarce data is normalized through coding to gain consistency for accurate results using synthetic minority oversampling technique. The proposed model is optimized and fine-tuned in an end to end manner to select ideal parameters using tree parzen estimator to build a probabilistic model. The patient’s medication is pigeonholed to plot the diabetic condition’s risk factor through an algorithm to classify blood glucose metrics using a modern surveillance error grid method. The proposed model can efficiently train, validate, and test noisy data by obtaining consistent results around 90% over the state of the art machine and deep learning techniques and overcoming the insufficiency in training data through transfer learning. The overall results of the proposed model are further tested with secondary datasets to verify model sustainability.

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Citations