Psychological Distress and the Role of Health Locus of Control in Lung Cancer Patients during the Covid-19 Pandemic | Chapter 11 | Current Progress in Medicine and Medical Research Vol. 7

 Aim: To prospectively review the prevalence of psychological distress knowing by lung malignancy patients during COVID-19 universal, the factors providing in its increase, and the impact of health position of control on it.Materials and Methods: 160 consecutive lung tumor outpatients were studied, at the Oncology Clinic of Chest Diseases General Hospital “Sotiria”, Athens, Greece.  Health behaviours and subjective distress were assessed using the Health Locus of Control Scale (HLOCS) and the Distress Thermometer (DT), individually. Demographic and medical dossier were retrieved through medical records.Results: The mean age of the sample was 66.2 age and the majority was men, accompanying chemotherapy being ultimate frequents treatment modality. The most frequent material problems were fatigue (63.7%), breathing (48.1%), and sleep disorders (45.0%). Nervousness (41.3%), tension (46.9%), fears (21.9%) and grief (29.4%) were ultimate prevalent emotional questions. Women experienced overall distress at a better extent compared to husbands (p .012), with significant distinctnesses in physical (p=.005), affecting (p=.001), family (p= .011) and practical questions (p=.008), and more specifically accompanying treatment-related determinations (p=.000), depression (p=.000), fears (p=.000), grief (p=.000), otherworldly/religious worries (p=.008) and presentation (p=.000) A strong positive equating was found 'tween all DT subscales (p .012). HLOC tended to be outside since distress was positively troubled by the subscales “doctors” and “chance”, displaying that patients rely upon these two factors to control their disease alternatively themselves. HLOC was also definitely correlated with the attendance of psychological questions (p < .05), with a difference middle from two points the two sexes, with husbands having larger scores in iHLOC compared to women (p=.004).Conclusions: External HLOC has a negative affect psychological distress, that in turn is affected by cooperative emotional and physical questions. These should stop living into account when treating bronchi cancer sufferers amidst unpredictable positions and frequent screening should be used, in order to improve lung cancer cases’ quality of existence.

Author(s) Details:

Frosyna Anagnosti,
Nursing Department, “Sotiria” Athens’ Chest Diseases Hospital, Athens, Greece and Department of Biomedical Sciences, University of West Attica, Athens, Greece.

Georgia Hardavella,
9th Department of Respiratory Medicine, “Sotiria” Athens’ Chest Diseases Hospital, Athens, Greece.

Dimitra Lekka,
Department of Psychiatry, “Sotiria” Athens’ Chest Diseases Hospital, Athens, Greece.

Dimitra Darahani,
Nursing Department, “Sotiria” Athens’ Chest Diseases Hospital, Athens, Greece and Department of Biomedical Sciences, University of West Attica, Athens, Greece.

Fragkiski Anthouli-Anagnostopoulou,
Department of Biomedical Sciences, University of West Attica, Athens, Greece.

Petros Papagiorgis,
Department of Biomedical Sciences, University of West Attica, Athens, Greece.

Dimitrios Chaniotis,
Department of Biomedical Sciences, University of West Attica, Athens, Greece.

Nikolaos Thalassinos,
Department of Biomedical Sciences, University of West Attica, Athens, Greece.

Please see the link here: https://stm.bookpi.org/CPMMR-V7/article/view/11599

Bank Term Deposit Subscription Recommendation Using Cross-validated Neural Network | Chapter 1 | New Innovations in Economics, Business and Management Vol. 2

 Term deposits can help to accelerate the financial field by increasing profit for both the bank and the customer. The bank's promotion efforts, as well as the customer's background information, often affect term deposit subscription. If the bank can identify clients' subscription tendencies early on, it can change its fundamental strategy to attract additional customers. The current work has focused on identifying term likelihood prediction from the customer's perspective in this setting. Machine learning-based algorithms have been used to predict term deposit investment opportunities in advance for this study. Neural network, a common machine learning-based method, is proposed as the predictive model in this study, along with stratified 10-fold cross-validation methodology. Other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP) are also implemented and compared to assess the efficiency of this model. With an accuracy of 88.32 percent and an MSE of 0.1168, this comparison analysis found that the suggested model outperforms existing baseline models in terms of prediction results.

Author(S) Details

Shawni Dutta
Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India.

Payal Bose
Lincoln University College, Kota Bharu, Kelantan, Malaysia.

Samir Kumar Bandyopadhyay
Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India.

View Book:- https://stm.bookpi.org/NIEBM-V2/article/view/4737