Application of machine learning models in predicting length of stay among healthcare workers in underserved communities in South Africa

Table 2 Predictions of length of stay across the three models

	Less than 1 year	Less than 2 years	Less than 3 years	More than 3 years
Multinomial logistic techniques
Sensitivity	0.7685	0.3248	0.0369	0.5425
Specificity	0.6548	0.8503	0.9766	0.7896
Positive predictive value	0.5728	0.4533	0.2340	0.3700
Negative predictive value	0.8244	0.7673	0.8398	0.8834
Balanced accuracy	0.7166	0.5876	0.5068	0.6661
Decision tree techniques
Sensitivity	0.7858	0.3740	0.000	0.4897
Specificity	0.6469	0.8075	1.000	0.8150
Positive predictive value	0.5728	0.4260	NaN	0.3761
Negative predictive value	0.8337	0.7716	0.8379	0.8751
Balanced accuracy	0.7164	0.5908	0.5000	0.6524
Naive Bayes techniques
Sensitivity	0.7728	0.2658	0.0403	0.5630
Specificity	0.6391	0.8752	0.9760	0.7675
Positive predictive value	0.5633	0.4485	0.2449	0.3556
Negative predictive value	0.8236	0.7573	0.8401	0.8852
Balanced accuracy	0.7059	0.5704	0.5081	0.6653

ISSN: 1478-4491