Happiness has become a major concern across many disciplines starting form public policy, economics and psychology because of the effects that come with not being happy. Psychologist would want to know the effects of low levels of happiness, economist would want to know the effects of levels of happiness in to the market place, researchers from health would be concerned with effects of high and low levels of happiness to health status. While predominantly, people had just a philosophical notion about happiness, currently there are numerous scientific studies on happiness. Approaches like cluster analysis have been employed before. This research used neural networks to classify multinomial levels of happiness of Kenyan youths by considering life style aspects of current life such as Internet usage, Physical activeness, Health, Social life, Education, Income, Country’s top leadership, Dining and Sleeping Habits. The research was able to fit a 14-1-4 neural network model to classify levels of happiness in Kenyan youths, an accuracy of 73% was achieved. The data was randomly split in to 70% training set and 30% test set. The training set was balanced using SMOTE approach. This research trained the model by applying gradient descent using error back propagation algorithm with initial weights drawn from uniform distribution and applied softmax activation function. Accuracy metrics were confusion matrix, precision and recall for each level of happiness, and F-Scores. The top leading factor related to happiness positively was physical activeness with youths who were more active being happier. The second factor was relationship type, the married youths were happier than the singles, separated or engaged. Youths who were more satisfied with their relationship, they were happier. Health was also positively related to happiness. On the other hand, the number of hours a youth spent on social media negatively affected their levels of happiness. The more the number of hours the low levels of happiness.
| Published in | International Journal of Data Science and Analysis (Volume 8, Issue 2) |
| DOI | 10.11648/j.ijdsa.20220802.16 |
| Page(s) | 59-71 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Happiness, Neural Network, Multinomial, Training, Cross-Entropy, Confusion Matrix, F-Score, Variable Importance
| [1] | Beata. S. (2020). PositivePsychology.com available at https://positivepsychology.com/team/beata-souders/ |
| [2] | Egilmez, G., Erdil, N. Ö., Arani, O. M., & Vahid, M. (2019). Application of artificial neural networks to assess student happiness. International Journal of Applied Decision Sciences, 12 (2), 115-140. |
| [3] | Fellows, E. W. (1966). Happiness: A survey of research. Journal of Humanistic Psychology, 6 (1), 17-30. |
| [4] | Fincham, J. E. (2008). Response rates and responsiveness for surveys, standards, and the Journal. American journal of pharmaceutical education, 72 (2). |
| [5] | Flynn, D. M., & MacLeod, S. (2015). Determinants of happiness in undergraduate university students. College Student Journal, 49 (3), 452-460. |
| [6] | Jaques, N., Taylor, S., Azaria, A., Ghandeharioun, A., Sano, A., & Picard, R. (2015, September). Predicting students' happiness from physiology, phone, mobility, and behavioral data. In 2015 International Conference on Affective Computing and Intelligent Interaction (ACII) (pp. 222-228). IEEE. |
| [7] | Jo, H. K., Kim, H. K., & Jeong, J. N. (2020). Factors affecting happiness among rural residents: a cross sectional survey. Community mental health journal, 56 (5), 915-924. |
| [8] | Jurafsky, D., & Martin, J. H. (2018). Speech and language processing (draft). Preparation [cited 2020 June 1] Available from: https://web. stanford. edu/~ jurafsky/slp3. |
| [9] | Layard, R. (2005). Happiness: Lessons from a New Science. Professor of Economics and Director of the Center for Economic Performance Penguin Press ISBN 1594200394, 9781594200397, illustrated edition. |
| [10] | Lee, M. A., & Kawachi, I. (2019). The keys to happiness: Associations between personal values regarding core life domains and happiness in South Korea. PloS one, 14 (1), e0209821. |
| [11] | Mehrdadi, A., Sadeghian, S., Direkvand-Moghadam, A., & Hashemian, A. (2016). Factors affecting happiness: a cross-sectional study in the Iranian youth. Journal of clinical and diagnostic research: JCDR, 10 (5), VC01. |
| [12] | Napierala, Tomasz. (2014). Re: How can we determine the sample size from an unknown population? Retrieved from: https://www.researchgate.net/post/How-can-we-determine-the-sample-size-from-an-unknown-population/53da14d2d3df3ef2338b45b9/citation/download. |
| [13] | Oberoi, S., Yang, J., Woodgate, R. L., Niraula, S., Banerji, S., Israels, S. J., Altman, G., Beattie, S., Rabbani, R., Askin, N., Gupta, A., Sung, L., Abou-Setta, A. M., & Zarychanski, R. (2020). Association of Mindfulness-Based Interventions With Anxiety Severity in Adults With Cancer: A Systematic Review and Meta-analysis. JAMA network open, 3 (8), e2012598. https://doi.org/10.1001/jamanetworkopen.2020.12598 |
| [14] | Olden, J. D., Joy, M. K., & Death, R. G. (2004). An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological modelling, 178 (3-4), 389-397. |
| [15] | Sacks, D. W., Stevenson, B., & Wolfers, J. (2010). Subjective well-being, income, economic development and growth (No. w16441). National Bureau of Economic Research. |
| [16] | Steptoe, A. (2019). Happiness and health. Annual Review of Public Health, 40, 04 doi: 10.1146/annurev-publhealth-040218-044150. |
APA Style
Martin Kinyua Ngari, Anthony Kibera Wanjoya, John Mwaniki Kihoro. (2022). Prediction of Levels of Happiness Using Multinomial Logit as a Neural Network: Evidence in Kenyan Youths. International Journal of Data Science and Analysis, 8(2), 59-71. https://doi.org/10.11648/j.ijdsa.20220802.16
ACS Style
Martin Kinyua Ngari; Anthony Kibera Wanjoya; John Mwaniki Kihoro. Prediction of Levels of Happiness Using Multinomial Logit as a Neural Network: Evidence in Kenyan Youths. Int. J. Data Sci. Anal. 2022, 8(2), 59-71. doi: 10.11648/j.ijdsa.20220802.16
AMA Style
Martin Kinyua Ngari, Anthony Kibera Wanjoya, John Mwaniki Kihoro. Prediction of Levels of Happiness Using Multinomial Logit as a Neural Network: Evidence in Kenyan Youths. Int J Data Sci Anal. 2022;8(2):59-71. doi: 10.11648/j.ijdsa.20220802.16
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ijdsa.20220802.16,
author = {Martin Kinyua Ngari and Anthony Kibera Wanjoya and John Mwaniki Kihoro},
title = {Prediction of Levels of Happiness Using Multinomial Logit as a Neural Network: Evidence in Kenyan Youths},
journal = {International Journal of Data Science and Analysis},
volume = {8},
number = {2},
pages = {59-71},
doi = {10.11648/j.ijdsa.20220802.16},
url = {https://doi.org/10.11648/j.ijdsa.20220802.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20220802.16},
abstract = {Happiness has become a major concern across many disciplines starting form public policy, economics and psychology because of the effects that come with not being happy. Psychologist would want to know the effects of low levels of happiness, economist would want to know the effects of levels of happiness in to the market place, researchers from health would be concerned with effects of high and low levels of happiness to health status. While predominantly, people had just a philosophical notion about happiness, currently there are numerous scientific studies on happiness. Approaches like cluster analysis have been employed before. This research used neural networks to classify multinomial levels of happiness of Kenyan youths by considering life style aspects of current life such as Internet usage, Physical activeness, Health, Social life, Education, Income, Country’s top leadership, Dining and Sleeping Habits. The research was able to fit a 14-1-4 neural network model to classify levels of happiness in Kenyan youths, an accuracy of 73% was achieved. The data was randomly split in to 70% training set and 30% test set. The training set was balanced using SMOTE approach. This research trained the model by applying gradient descent using error back propagation algorithm with initial weights drawn from uniform distribution and applied softmax activation function. Accuracy metrics were confusion matrix, precision and recall for each level of happiness, and F-Scores. The top leading factor related to happiness positively was physical activeness with youths who were more active being happier. The second factor was relationship type, the married youths were happier than the singles, separated or engaged. Youths who were more satisfied with their relationship, they were happier. Health was also positively related to happiness. On the other hand, the number of hours a youth spent on social media negatively affected their levels of happiness. The more the number of hours the low levels of happiness.},
year = {2022}
}
TY - JOUR T1 - Prediction of Levels of Happiness Using Multinomial Logit as a Neural Network: Evidence in Kenyan Youths AU - Martin Kinyua Ngari AU - Anthony Kibera Wanjoya AU - John Mwaniki Kihoro Y1 - 2022/04/26 PY - 2022 N1 - https://doi.org/10.11648/j.ijdsa.20220802.16 DO - 10.11648/j.ijdsa.20220802.16 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 59 EP - 71 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20220802.16 AB - Happiness has become a major concern across many disciplines starting form public policy, economics and psychology because of the effects that come with not being happy. Psychologist would want to know the effects of low levels of happiness, economist would want to know the effects of levels of happiness in to the market place, researchers from health would be concerned with effects of high and low levels of happiness to health status. While predominantly, people had just a philosophical notion about happiness, currently there are numerous scientific studies on happiness. Approaches like cluster analysis have been employed before. This research used neural networks to classify multinomial levels of happiness of Kenyan youths by considering life style aspects of current life such as Internet usage, Physical activeness, Health, Social life, Education, Income, Country’s top leadership, Dining and Sleeping Habits. The research was able to fit a 14-1-4 neural network model to classify levels of happiness in Kenyan youths, an accuracy of 73% was achieved. The data was randomly split in to 70% training set and 30% test set. The training set was balanced using SMOTE approach. This research trained the model by applying gradient descent using error back propagation algorithm with initial weights drawn from uniform distribution and applied softmax activation function. Accuracy metrics were confusion matrix, precision and recall for each level of happiness, and F-Scores. The top leading factor related to happiness positively was physical activeness with youths who were more active being happier. The second factor was relationship type, the married youths were happier than the singles, separated or engaged. Youths who were more satisfied with their relationship, they were happier. Health was also positively related to happiness. On the other hand, the number of hours a youth spent on social media negatively affected their levels of happiness. The more the number of hours the low levels of happiness. VL - 8 IS - 2 ER -
Information
Email: