Machine Learning Algorithms for Predicting Stunting among Under-Five Children in Papua New Guinea
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Source
2.2. Outcome Variable and Potential Risk Factors
2.3. Analytic Strategy
2.3.1. Preprocessing
2.3.2. Feature Selection
2.3.3. Machine Learning Algorithms and Hyperparameter Tuning
2.3.4. Model Performance Evaluation
3. Results
3.1. Descriptive Results
3.2. Feature Selection Results
3.3. Hyperparameter Tuning Results
3.4. Evaluation of the Prediction Models
3.5. Model Interpretation
3.5.1. SHAP Summary Plots
3.5.2. SHAP Dependence Plot of Child’s Age
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Hyperparameter | Range | Type |
---|---|---|
Eta | (0.01, 0.3) | Real |
Gamma | (0, 0.2) | Real |
Subsample | (0.1, 1) | Real |
Colsample bytree | (0.1, 1) | Real |
Nrounds | [1, 200] | Integer |
Maxdepth | [1, 20] | Integer |
Min child weight | [1, 20] | Integer |
Stunted | |||||
---|---|---|---|---|---|
Variables | N | Frequency (%)/Mean (SD) | No (%) | Yes (%) | p-Values |
Individual characteristics | |||||
Child’s age (months) | 3380 | 29.73 | <0.001 | ||
Child’s gender | <0.01 | ||||
Male | 1795 | 53.11 | 57.83 | 42.17 | |
Female | 1585 | 46.89 | 63.09 | 36.91 | |
Birth size | <0.001 | ||||
Average | 1215 | 38.36 | 65.93 | 34.07 | |
Large | 1337 | 42.22 | 60.13 | 39.87 | |
Small | 615 | 19.42 | 51.22 | 48.78 | |
Birth order | 3380 | 3.15 | 0.069 | ||
Duration of breastfeeding | <0.001 | ||||
Never breastfed | 168 | 7.19 | 60.71 | 39.29 | |
<6 months | 367 | 15.71 | 76.02 | 23.98 | |
≥6 months | 1801 | 77.10 | 59.74 | 40.26 | |
Early breastfeeding | 0.280 | ||||
No | 919 | 41.81 | 63.87 | 36.13 | |
Yes | 1279 | 58.19 | 61.61 | 38.39 | |
Had diarrhea in the past 2 weeks | 0.925 | ||||
No | 2683 | 84.74 | 60.27 | 39.73 | |
Yes | 483 | 15.26 | 60.04 | 39.96 | |
Had fever in the past 2 weeks | 0.867 | ||||
No | 2489 | 78.72 | 60.39 | 39.61 | |
Yes | 673 | 21.28 | 60.03 | 39.97 | |
Maternal characteristics | |||||
Maternal age (years) | 3380 | 30.16 | 0.848 | ||
Partner’s age (years) | 2961 | 35.03 | 0.547 | ||
Maternal employment status | <0.001 | ||||
Not employed | 2101 | 62.59 | 57.45 | 42.55 | |
Employed | 1256 | 37.41 | 64.81 | 35.19 | |
Partner’s employment status | <0.001 | ||||
Not employed | 1328 | 43.60 | 55.72 | 44.28 | |
Employed | 1718 | 56.40 | 63.50 | 36.50 | |
Maternal occupation | <0.001 | ||||
No occupation | 2124 | 63.46 | 57.63 | 42.37 | |
Professional/technical/managerial | 161 | 4.81 | 78.88 | 21.12 | |
Clerical | 66 | 1.97 | 69.70 | 30.30 | |
Sales | 161 | 4.81 | 72.67 | 27.33 | |
Agricultural | 560 | 16.73 | 56.43 | 43.57 | |
Services | 257 | 7.68 | 68.87 | 31.13 | |
Skilled manual | 7 | 0.21 | 85.71 | 14.29 | |
Unskilled manual | 11 | 0.33 | 54.55 | 45.45 | |
Maternal marital status | 0.456 | ||||
Never Married/divorced/separated | 274 | 8.11 | 62.41 | 37.59 | |
Married/living together | 3106 | 91.89 | 60.11 | 39.89 | |
Maternal religion | 0.843 | ||||
Non-Christian/no religion | 29 | 0.86 | 58.62 | 41.38 | |
Christian | 3343 | 99.14 | 60.42 | 39.58 | |
Maternal education level | <0.001 | ||||
No education | 647 | 19.14 | 48.53 | 51.47 | |
Primary education | 1704 | 50.41 | 59.10 | 40.90 | |
Secondary education | 918 | 27.16 | 69.17 | 30.83 | |
Higher education | 111 | 3.28 | 73.87 | 26.13 | |
Partner’s education level | |||||
No education | 458 | 15.17 | 46.72 | 53.28 | |
Primary education | 1371 | 45.40 | 58.35 | 41.65 | |
Secondary education | 953 | 31.56 | 64.85 | 35.15 | |
Higher education | 238 | 7.88 | 76.05 | 23.95 | |
Exposure to mass media | <0.001 | ||||
No | 1646 | 49.03 | 53.95 | 46.05 | |
Yes | 1711 | 50.97 | 66.69 | 33.31 | |
Maternal age of first birth (years) | 3380 | 21.17 | <0.01 | ||
Household characteristics | |||||
Sex of househead | 0.061 | ||||
Male | 2892 | 85.56 | 59.65 | 40.35 | |
Female | 488 | 14.44 | 64.14 | 35.86 | |
Household wealth | <0.001 | ||||
Poorest | 556 | 16.45 | 46.40 | 53.60 | |
Poorer | 531 | 15.71 | 49.91 | 50.09 | |
Middle | 653 | 19.32 | 60.49 | 39.51 | |
Richer | 809 | 23.93 | 61.80 | 38.20 | |
Richest | 831 | 24.59 | 74.61 | 25.39 | |
Number of under-5 children | 3380 | 3.35 | <0.05 | ||
Number of household members | 3380 | 6.93 | <0.05 | ||
Type of toilet facility | <0.001 | ||||
No facility | 683 | 20.47 | 61.35 | 38.65 | |
Unimproved | 1579 | 47.33 | 54.40 | 45.60 | |
Improved | 1074 | 32.19 | 68.53 | 31.47 | |
Source of drinking water | <0.001 | ||||
Unimproved | 1558 | 46.18 | 54.36 | 45.64 | |
Improved | 1816 | 53.82 | 65.42 | 34.58 | |
Type of cooking fuels | <0.001 | ||||
Polluting fuels | 3058 | 91.64 | 58.70 | 41.30 | |
Clean fuels | 279 | 8.36 | 78.85 | 21.15 | |
Distance to health facility | <0.001 | ||||
Not a big problem | 1509 | 45.14 | 64.88 | 35.12 | |
Big problem | 1834 | 54.86 | 56.32 | 43.68 | |
Community characteristics | |||||
Region | <0.001 | ||||
Southern Region | 663 | 19.62 | 65.68 | 34.32 | |
Highland Region | 1043 | 30.86 | 41.03 | 58.97 | |
Momase Region | 799 | 23.64 | 59.32 | 40.68 | |
Islands Region | 875 | 25.89 | 69.37 | 30.63 | |
Area | <0.001 | ||||
Rural | 2581 | 76.36 | 56.95 | 43.05 | |
Urban | 799 | 23.64 | 71.09 | 28.91 |
Trainset (Cross-Validation) | ||
---|---|---|
Models | Optimal Hyperparameters | AUC |
None | ||
CTree | maxdepth = 5, mincriterion = 0.950 | 0.639 |
XGBoost | nrounds = 12, eta = 0.153, gamma = 0.091, subsample = 0.807, colsample bytree = 0.995, maxdepth = 6, min child weight = 5 | 0.644 |
SVM-RBF | C = 2−5, σ = 2−15 | 0.658 |
LASSO | ||
CTree | maxdepth = 7, mincriterion = 0.900 | 0.642 |
XGBoost | nrounds = 12, eta = 0.012, gamma = 0.199, subsample = 0.694, colsample bytree = 0.811, maxdepth = 7, min child weight = 13 | 0.653 |
SVM-RBF | C = 215, σ = 2−15 | 0.671 |
RF-RFE | ||
CTree | maxdepth = 4, mincriterion = 0.990 | 0.646 |
XGBoost | nrounds = 19, eta = 0.149, gamma = 0.058, subsample = 0.909, colsample bytree = 1, maxdepth = 20, min child weight = 18 | 0.666 |
SVM-RBF | C = 2−1, σ = 2−5 | 0.666 |
Models | Test Set | |||||
---|---|---|---|---|---|---|
Metric | AUC (95% CI) | Accuracy | Precision | Recall | F1 Score | Threshold |
None | ||||||
LR | 0.728 (0.672–0.785) | 0.675 | 0.731 | 0.559 | 0.633 | 0.370 |
CTree | 0.695 (0.639–0.750) | 0.630 | 0.669 | 0.515 | 0.582 | 0.426 |
XGBoost | 0.744 (0.690–0.798) | 0.707 | 0.762 | 0.593 | 0.667 | 0.400 |
SVM-RBF | 0.704 (0.646–0.761) | 0.672 | 0.692 | 0.559 | 0.619 | 0.363 |
LASSO | ||||||
LR | 0.730 (0.674–0.787) | 0.692 | 0.708 | 0.582 | 0.639 | 0.391 |
CTree | 0.736 (0.682–0.789) | 0.683 | 0.700 | 0.572 | 0.630 | 0.459 |
XGBoost | 0.767 (0.714–0.819) | 0.728 | 0.715 | 0.628 | 0.669 | 0.487 |
SVM-RBF | 0.722 (0.666–0.778) | 0.672 | 0.677 | 0.561 | 0.613 | 0.346 |
RF-RFE | ||||||
LR | 0.731 (0.676–0.785) | 0.695 | 0.685 | 0.589 | 0.633 | 0.394 |
CTree | 0.726 (0.672–0.781) | 0.681 | 0.723 | 0.566 | 0.635 | 0.343 |
XGBoost | 0.752 (0.698–0.806) | 0.710 | 0.723 | 0.603 | 0.657 | 0.388 |
SVM-RBF | 0.729 (0.674–0.785) | 0.692 | 0.615 | 0.597 | 0.606 | 0.367 |
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Shen, H.; Zhao, H.; Jiang, Y. Machine Learning Algorithms for Predicting Stunting among Under-Five Children in Papua New Guinea. Children 2023, 10, 1638. https://doi.org/10.3390/children10101638
Shen H, Zhao H, Jiang Y. Machine Learning Algorithms for Predicting Stunting among Under-Five Children in Papua New Guinea. Children. 2023; 10(10):1638. https://doi.org/10.3390/children10101638
Chicago/Turabian StyleShen, Hao, Hang Zhao, and Yi Jiang. 2023. "Machine Learning Algorithms for Predicting Stunting among Under-Five Children in Papua New Guinea" Children 10, no. 10: 1638. https://doi.org/10.3390/children10101638