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Optimized Predictive Models in Health Care Using Machine Learning
book

Optimized Predictive Models in Health Care Using Machine Learning

by Sandeep Kumar, Anuj Sharma, Navneet Kaur, Lokesh Pawar, Rohit Bajaj
March 2024
Intermediate to advanced content levelIntermediate to advanced
384 pages
9h 52m
English
Wiley-Scrivener
Content preview from Optimized Predictive Models in Health Care Using Machine Learning

3Improving Accuracy in Predicting Stress Levels of Working Women Using Convolutional Neural Networks

Purude Vaishali Narayanro1,2, Regula Srilakshmi1, M. Deepika1 and P. Lalitha Surya Kumari2*

1Department of CSE, Neil Gogte Institute of Technology, Hyderabad, Telangana, India

2Department of CSE, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, India

Abstract

Currently, the world is facing a severe and prevalent issue called stress, which significantly impacts women’s health and the development of their children. To aid working women in their professional and personal growth, assessing their stress levels accurately is crucial. Artificial intelligence (AI) algorithms have been used for stress level prediction. However, these models are prone to misclassification and errors, and their design can be complicated and less efficient. To overcome the limitations, we propose a convolutional neural network (CNN) model to identify stress levels in working women. Our framework includes creating a dataset, extracting the features, selecting optimal features, and binary classification using CNNs. We also handle missing values and remove duplicate attributes during data preprocessing. Our approach using CNNs is expected to outperform previous ML and DL algorithms in accurately predicting stress levels in working women.

Keywords: Stress prediction, working women, classification, CNN

3.1 Introduction

Predictive modeling uses statistics and machine learning algorithms to create ...

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Publisher Resources

ISBN: 9781394174621Purchase Link