Title: A machine learning-based intelligent system for women's work-life balance prediction and a smart mobile application for women's daily life assistance

Authors: Urshi Barua Teya; Mahfuzulhoq Chowdhury

Addresses: Computer Science and Engineering Department, Chittagong University of Engineering and Technology, Chittagong, Bangladesh ' Computer Science and Engineering Department, Chittagong University of Engineering and Technology, Chittagong, Bangladesh

Abstract: Work-life imbalance can lead to several problems for a person, like personal stress, anxiety or depression. At present, there is a lack of a work-life balance prediction system using Machine Learning (ML) and a mobile application for the daily life assistance of Bangladeshi women. This paper determines the work-life balance status of working women in Bangladesh using machine learning. We tried to build a mobile application that gives some features that can help a working woman live a balanced work-life balance, such as work remainder, doctors' information and police complaints. Different kinds of ML Classification algorithms were used for the work-life balance status determination of women. This paper prepares a data set of 22 attributes for appropriately classifying work-life balance status. Seven ML methods are tested using the data set. This paper has suggested K-Nearest Neighbour be the most effective, with a classification accuracy of 98% among all compared methods.

Keywords: work-life balance; machine learning; eligibility prediction system; police reporting; work remainder; Bangladeshi women; mobile application.

DOI: 10.1504/IJSMARTTL.2023.136915

International Journal of Smart Technology and Learning, 2023 Vol.3 No.3/4, pp.325 - 354

Received: 16 Jun 2023
Accepted: 01 Nov 2023
Published online: 28 Feb 2024 *

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