https://journal.achsm.org.au/index.php/achsm <p>The Asia Pacific Journal of Health Management (APJHM) is a peer-reviewed journal for managers of organisations offering healthcare and aged care services. The APJHM aims to promote the discipline of health management throughout the region by facilitating the transfer of knowledge among readers by widening the evidence base for management practices.<br /><br />*Print 1(1);2006 - 5(1);2010 Online 4(2);2009 - current<br />*ISSN 2204-3136 (online); ISSN 1833-3818 (print)</p> Australasian College of Health Service Management en-US Asia Pacific Journal of Health Management 1833-3818 https://journal.achsm.org.au/index.php/achsm/article/view/3593 <p><strong><em>Purpose: </em></strong>The aim of this study is to determine which patients are at risk of lung cancer with a sample of 1000 people living in China and a data set consisting of 20 variables. In this direction, it is aimed to take preventive measures for individuals at risk of developing lung cancer, to protect human health, and to contribute to the effective and efficient use of resources. In this way, the model developed in the planning of preventive and therapeutic activities of professionals working in the health sector will be exemplary as a decision model.</p> <p><strong><em>Methodology</em></strong><strong>: </strong>A linear regression model was developed and applied to predict individuals at high risk of developing lung cancer using machine learning on the Microsoft Azure Machine Learning Studio platform.</p> <p><strong><em>Findings: </em></strong>Lung cancer risk of the patients in the sample was predicted with the machine learning application. It was determined that these predictions made with machine learning gave an effective result and provided efficient policies. The developed model and application example is proposed as a modern, valid and artificial intelligence-based decision-making model for professionals.</p> <p><strong><em>Originality: </em></strong>There are few statistical analysis and machine learning applications for lung cancer prediction in the literature. In this paper, in addition to the prediction model, statistical analyses will be performed, preventive measures will be proposed, and the scope of the analyses will be made holistic by linking it to efficiency in resource utilization. As a result, this will enable professionals to make optimal decisions.</p> Serkan Derici Copyright (c) https://journal.achsm.org.au/index.php/achsm/article/view/3591 <p>&nbsp;</p> <p>This research is an attempt to address the individual differences; age, gender, marital status, work experience, and educational qualification affect the fear of missing out (FOMO) of students. FOMO is the anxiety perceived among the individuals who spent time on social media when they feel missed or perceive that their fellow mates are experiencing, doing something which is somewhat rewarding that they have missed or are not able to experience. The FOMO was evaluated with the three dimensions namely Self Esteem, Social Interaction/Extraversion, and Social Anxiety. Data were collected from 285 students and significant differences were found in age, gender, marital status, work experience, educational qualification, and FOMO. This paper presents the findings with all three dimensions of FOMO for students enrolled in B-schools.</p> Shivani Agarwal Rekha Mewafarosh Copyright (c)