Open Access

ARTICLE

Harnessing LSTM Classifier to Suggest Nutrition Diet for Cancer Patients

S. Raguvaran^1,*, S. Anandamurugan², A. M. J. Md. Zubair Rahman³

1 Department of Computer Science and Engineering, Al-Ameen Engineering College, Erode, 638115, India
2 Department of Information and Technology, Kongu Engineering College, Erode, 638060, India
3 Al-Ameen Engineering College, Erode, 638115, India

* Corresponding Author: S. Raguvaran. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 2171-2187. https://doi.org/10.32604/iasc.2023.028605

Received 14 February 2022; Accepted 19 April 2022; Issue published 19 July 2022

Abstract

A customized nutrition-rich diet plan is of utmost importance for cancer patients to intake healthy and nutritious foods that help them to be strong enough to maintain their body weight and body tissues. Consuming nutrition-rich diet foods will prevent them from the side effects caused before and after treatment thereby minimizing it. This work is proposed here to provide them with an effective diet assessment plan using deep learning-based automated medical diet system. Hence, an Enhanced Long-Short Term Memory (E-LSTM) has been proposed in this paper, especially for cancer patients. This proposed method will be very useful for cancer patients as this would help them predict the foods which can be consumed by them based on the nutrition analysis of food images. The classification will be performed in E-LSTM by analyzing the two datasets, one with food images and another with cancer patients’ details. Following an in-depth analysis of the major research papers concerning deep learning strategies to identify the foods along with their nutrition composition, this method has been identified as one of the finest deep learning approaches that are used for classification especially. This work has been identified as the first work producing a new layer for feature extraction and providing nutrition suggestions, especially for cancer patients using the LSTM technique. The accuracy of prediction and classification will be improved by the dedicated layer for feature extraction in E-LSTM. Hence, it is proved that this proposed method outperforms all other existing techniques in terms of F1 Score, Precision, Recall, Classification accuracy, Training loss and Validation loss.

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Keywords

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