A Comparative Study of Machine Learning Techniques for Health Prediction
Rosemary Varghese
, Anila S , Shyama R
Decision Tree (DT); Depression, Anxiety, Stress (DASS- 21); K Naïve Bayes(NB);Machine Learning
Personal well-being refers to both physical as well as mental fitness. In the current scenario of expeditious commercial growth and pandemics, the human race is also challenged by immense psychological pressures. This paper presents the prediction of the most pertinent psychological issues identified by the World Health Organization – Anxiety,Stress, and Depression. Machine Learning algorithms are used for the prediction of the same. The data was previously collected from people in various economic,cultural, and social situations through the Depression,Anxiety, and Stress Scale Questionnaire (DASS21). Three supervised learning algorithms were applied and corresponding confusion matrices were calculated. The accuracies of each model were compared and were found that the model with the best accuracy is K-Nearest-Neighbor.In addition, analysis of the results divulged that the models were sensitive to negative results.
"A Comparative Study of Machine Learning Techniques for Health Prediction", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 5, page no.325 - 330, May-2022, Available :https://ijsdr.org/papers/IJSDR2205062.pdf
Volume 7
Issue 5,
May-2022
Pages : 325 - 330
Paper Reg. ID: IJSDR_200334
Published Paper Id: IJSDR2205062
Downloads: 000347258
Research Area: Engineering
Country: -, -, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
