Predictive modelling of linear growth faltering among pediatric patients with Diarrhea in Rural Western Kenya: an explainable machine learning approach

Table 3 Model performance of linear growth faltering prediction ^β models using original training data only

Algorithm^*	Sensitivity % [95% CI]	Specificity % [95% CI]	PPV % [95% CI]	NPV % [95% CI]	F1-Score [95% CI]	AUC % [95% CI]	PRAUC % [95% CI]
RF	52.2 [36.9–67.1]	78.6 [72.7–83.7]	32.9 [22.3–44.9]	89.1 [84.0–93.0]	40.3 [10.9–51.4]	70.3 [61.8–78.7]	90.6 [88.4–90.8]
GBM	80.4 [66.1–90.6]	63.3 [56.7–69.6]	30.6 [22.5–39.6]	94.2 [89.2–97.3]	44.3 [12.9–55.8]	75.5 [68.2–82.8]	93.6 [92.3–93.9]
NB	63.0 [47.5–76.8]	75.1 [69.0–80.6]	33.7 [23.9–44.7]	91.0 [86.0–94.7]	43.9 [5.6–60.3]	73.6 [66.1–81.2]	93.0 [91.1–94.0]
LR	73.9 [58.9–85.7]	63.3 [56.7–69.6]	28.8 [20.8–37.9]	92.4 [87.0–96.0]	41.5 [7.8–52.1]	73.8 [67.0–80.5]	93.9 [92.0–94.9]
SVM	71.7 [56.5–84.0]	65.9 [59.4–72.1]	29.7 [21.4–39.1]	92.1 [86.8–95.7]	42.0 [7.2–51.9]	75.2 [68.9–81.5]	94.4 [93.0–95.5]
KNN	82.6 [68.6–92.2]	56.8 [50.1–63.3]	27.7 [20.4–36.0]	94.2 [88.9–97.5]	41.5 [12.2–51.8]	73.1 [66.3–79.9]	93.6 [91.4–94.2]
ANN	82.6 [68.6–92.2]	55.9 [49.2–62.4]	27.3 [20.1–35.5]	94.1 [88.7–97.4]	41.1 [12.0–57.9]	68.0 [60.5–75.6]	91.4 [89.3–92.5]

95% CI 95% Confidence Interval, PPV Positive Predictive Value, NPV Negative Predictive Value, AUC Area under the Curve, PRAUC Precision Recall Area under the Curve
^*RF Random Forest; GBM-Gradient Boosting; NB- Naïve Bayes; LR-Logistic Regression; SVM- Support vector machine; KNN-K-nearest neighbors; ANN-Artificial Neural Networks
^β− Linear growth faltering defined as Δ HAZ ≥ − 0.5

ISSN: 1472-6947