Locality Filter All Mindanao records · 4500 of 4500 records
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Select a service area, service stage, and survey question. Advanced factor settings are optional.
Per-Question (Indicator-Level) Classification Review
Currently selected question
Vaccination for infants/children
The cards below place this question alongside other questions from the selected service stage, so its evidence strength can be compared fairly.
How to read this result
Start with “Ready for interpretation.” Treat “Use with caution” as supporting evidence, and do not make strong claims from indicators that need stronger evidence.
- Pre-natal/post-natal/child birth services
- Family Planning/ Reproductive Health Distribution of reproductive health supplies, information dissemination and other services
- Free Basic Medicine or Low-Cost Medicine Program
- Basic dental/oral hygiene
- Free General Consultations/Access to secondary and/or tertiary health care
- Prevention and Management of Communicable and Non-Communicable Diseases
- Vaccination for infants/children
Technical and methodology notes
Available data: The current dataset does not include direct access-barrier variables such as distance, waiting time, facility supply, staff interaction, or household service need. The portal should therefore improve the existing evidence first instead of inventing unavailable predictors.
Eligible responses: Stage-specific modeling is applied through the saved stage response files and skip-pattern handling: Awareness uses valid awareness responses; Availment uses the availment stage; Satisfaction and Need for Action are interpreted only for respondents who reached the service-use stage.
Predictor selection: The decision-tree pipeline now enriches the general profile and citizen-segment predictors with available service-context evidence when applicable: health background variables for Health Services, education background variables for Support to Education, and crime, disaster, corruption, and citizen-attitude evidence for Governance and Response. Feature selection remains part of the model search, while chi-square is kept in the citizen segment evidence area as profile-cluster support.
Model comparison: Recommended model comparison: keep Decision Tree as the official explanation model, then add Random Forest as a performance benchmark and Logistic Regression as a simple baseline. If another model improves ROC AUC or recall, report it as supporting evidence while keeping the decision tree for readable rules.
What the Model Found
For Health Services - Awareness, the summary combines 7 indicator-specific decision-tree model(s). Mean F1 is 73.3% and mean ROC AUC is 56.1%, so the result should be read as an overall tendency across indicators. The indicator-level rows remain the main evidence for decision-support use.
Use the mean scores for the general story; use the indicator rows for the actual evidence.
Stage eligibility is handled before modeling: Awareness uses all valid Yes/No responses; Availment uses Awareness = Yes; Satisfaction and Need for Action use Awareness = Yes and Availment = Yes. Skip-pattern values such as 95-99 are excluded from the target class.
- Mean precision is 86.6%, indicating strong positive predictions when an indicator model predicts the positive service outcome.
- Mean recall is 65.8%, meaning some indicator models may still miss actual positive cases, especially when recall is lower than precision.
- Mean F1 score is 73.3%, which balances precision and recall across the indicator models.
- Mean ROC AUC is 56.1%, showing how well the indicator models separate outcome groups across thresholds on average.
- Top predictors currently include MCA DimMagnitude, Age Group, Source of Information, MCA Dim1, Relationship to HH Head. These variables are useful signals for explaining which respondent profiles are linked to the selected service outcome.
- The current classification pipeline also includes up to 6 service-context predictor(s) for this view, so the model is no longer limited to general profile and cluster variables.
- Interpretation: the model has limited separation power. Use the predictors as exploratory clues and validate findings with descriptive charts and local service context.
Key Influencing Factors
-
Vaccination for infants/children
MCA DimMagnitude -
Vaccination for infants/children
Age Group -
Vaccination for infants/children
Source of Information -
Pre-natal/post-natal/child birth services
MCA Dim1 -
Pre-natal/post-natal/child birth services
Relationship to HH Head -
Pre-natal/post-natal/child birth services
Exploring Additional Public Health Facilities for Follow-Up Care -
Free General Consultations/Access to secondary and/or tertiary health care
MCA Dim1 -
Free General Consultations/Access to secondary and/or tertiary health care
Relationship to HH Head
Key-Factor Strength Chart
Show how strongly each available factor influenced the model result.
Optional detail
Key-Factor Strength Chart
Show how strongly each available factor influenced the model result.
Key Influencing Factors
Decision Tree Interpretation
Read the tree together with its plain-language insights.
Evidence Tables
Show readiness review, indicator metrics, rule reference, and diagnostics.
Optional detail
Evidence Tables
Show readiness review, indicator metrics, rule reference, and diagnostics.
Model Readiness Review
| Indicator | Signal Quality (F1 / ROC AUC / Recall / Baseline) | Meaning | Majority Response (Baseline Class) | Eligible Records | Baseline Accuracy (Majority Guess) | Model Accuracy | Gain (Model - Baseline) | Possible Cause | Suggested Data Improvement |
|---|---|---|---|---|---|---|---|---|---|
| D8_DHSii Pre-natal/post-natal/child birth services |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (92.8%) | 4350 | 92.8% | 77.8% | -14.9 pts | The result may be close to the majority-class baseline, meaning the model adds limited separation beyond the most common response. | Add or review awareness-source, barangay information channel, distance, and program availability variables for Health Services. |
| D8_DHSiii Free General Consultations/Access to secondary and/or tertiary health care |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | Yes / Positive (87.7%) | 4200 | 87.7% | 73.1% | -14.5 pts | The result may be close to the majority-class baseline, meaning the model adds limited separation beyond the most common response. | Add or review awareness-source, barangay information channel, distance, and program availability variables for Health Services. |
| D8_DHSvii Family Planning/ Reproductive Health Distribution of reproductive health supplies, information dissemination and other services |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (86.2%) | 4500 | 86.2% | 67.9% | -18.3 pts | The result may be close to the majority-class baseline, meaning the model adds limited separation beyond the most common response. | Add or review awareness-source, barangay information channel, distance, and program availability variables for Health Services. |
| D8_DHSv Prevention and Management of Communicable and Non-Communicable Diseases |
Limited signal | The model can be reviewed as an exploratory clue, but F1, ROC AUC, or baseline gain is not strong enough for confident prediction. | Yes / Positive (65.6%) | 4500 | 65.6% | 59.9% | -5.7 pts | The result may be close to the majority-class baseline, meaning the model adds limited separation beyond the most common response. | Add or review awareness-source, barangay information channel, distance, and program availability variables for Health Services. |
| D8_DHSiv Free Basic Medicine or Low-Cost Medicine Program |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (87.7%) | 4350 | 87.7% | 53.8% | -33.9 pts | The result may be close to the majority-class baseline, meaning the model adds limited separation beyond the most common response. | Add or review awareness-source, barangay information channel, distance, and program availability variables for Health Services. |
| D8_DHSvi Basic dental/oral hygiene |
Moderate signal | The model has some useful signal based on F1 and ROC AUC, but separation between outcome groups is still limited. | Yes / Positive (77.2%) | 3450 | 77.2% | 54.9% | -22.2 pts | The result may be close to the majority-class baseline, meaning the model adds limited separation beyond the most common response. | Add or review awareness-source, barangay information channel, distance, and program availability variables for Health Services. |
| D8_DHSi Vaccination for infants/children |
Needs more predictors | The model has difficulty separating respondents based on ROC AUC, recall, or F1. | Yes / Positive (95.2%) | 4500 | 95.2% | 44.2% | -51.1 pts | The result may be close to the majority-class baseline, meaning the model adds limited separation beyond the most common response. | Add or review awareness-source, barangay information channel, distance, and program availability variables for Health Services. |
Indicator-Level Metrics
The AVG values summarize 7 indicator model(s) for Health Services - Awareness. They describe the overall pattern across indicators, not the result of one specific indicator.
Average F1 is 73.3%, which gives the most balanced quick reading because it considers both correct positive predictions and missed positive cases.
Average ROC AUC is 56.1%, so the model should be read as decision-support evidence. Values closer to 50% mean the predictors have limited ability to separate the outcome groups.
Average accuracy is 61.7%, precision is 86.6%, and recall is 65.8%. Compare the indicator rows below to see which indicators are stronger or weaker than the AVG.
| Target | Indicator | Accuracy | Precision | Recall | F1 | ROC AUC | CV F1 | Best CV F1 | Selected Features | Base Predictors | Service Context | Total Predictors | Depth | Leaf | Criterion |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| D8_DHSii | Pre-natal/post-natal/child birth services | 77.8% | 93.3% | 82.0% | 87.3% | 55.4% | 80.7% | 85.9% | 16 | 17 | 6 | 23 | 3 | 35 | gini |
| D8_DHSiii | Free General Consultations/Access to secondary and/or tertiary health care | 73.1% | 88.2% | 80.1% | 83.9% | 51.8% | 73.8% | 80.8% | 16 | 17 | 6 | 23 | 5 | 10 | entropy |
| D8_DHSvii | Family Planning/ Reproductive Health Distribution of reproductive health supplies, information dissemination and other services | 67.9% | 88.7% | 72.0% | 79.5% | 57.8% | 78.4% | 84.3% | 8 | 17 | 6 | 23 | 3 | 35 | gini |
| D8_DHSv | Prevention and Management of Communicable and Non-Communicable Diseases | 59.9% | 66.9% | 77.1% | 71.6% | 52.3% | 58.9% | 70.7% | 8 | 17 | 6 | 23 | 3 | 10 | gini |
| D8_DHSiv | Free Basic Medicine or Low-Cost Medicine Program | 53.8% | 90.3% | 52.9% | 66.8% | 56.4% | 60.9% | 68.4% | 8 | 17 | 6 | 23 | 5 | 35 | gini |
| D8_DHSvi | Basic dental/oral hygiene | 54.9% | 81.5% | 53.8% | 64.8% | 57.4% | 64.7% | 75.8% | 16 | 17 | 6 | 23 | 3 | 10 | entropy |
| D8_DHSi | Vaccination for infants/children | 44.2% | 97.2% | 42.6% | 59.2% | 61.5% | 78.4% | 84.9% | all | 17 | 6 | 23 | 3 | 10 | gini |
Decision Rule Reference
| Code | Meaning | Code Values / Variable Type | How to Read the Split |
|---|---|---|---|
| A3.1 | Age Group | 1=18→24; 2=25→29; 3=30→34; 4=35→39; 5=40→44; 6=45→54; 7=55→64; 8=65→74; 9=75 and above | <= 7.5 means 18→24, 25→29, 30→34, 35→39, 40→44, 45→54, 55→64; > 7.5 means 65→74, 75 and above. |
| Dim2 | MCA profile dimension 2 | A combined respondent-profile score from MCA. It is not a single survey question. | Dim2 <= 0.434 follows one side of the profile map; values above 0.434 follow the other side. |
| B6 | Source of Information | 1=TELEVISION; 2=RADIO; 3=NEWSPAPER; 4=FAMILY/FRIENDS; 5=INTERNET; 6=MUNICIPAL GOVERNMENT; 7=BARANGAY OFFICIALS AND PERSONNEL; 99=OTHERS | <= 5.5 means TELEVISION, RADIO, NEWSPAPER, FAMILY/FRIENDS, INTERNET; > 5.5 means MUNICIPAL GOVERNMENT, BARANGAY OFFICIALS AND PERSONNEL, OTHERS. |
Gini Split Diagnostics
| Node | Type | Rule | Gini | Samples | Not Aware | Aware | Prediction |
|---|---|---|---|---|---|---|---|
| 0 | Split | A3.1 <= 7.500 | 0.5 | 4500 | 0.5 | 0.5 | Aware |
| 1 | Split | A3.1 <= 1.500 | 0.4978 | 3896 | 0.5 | 0.5 | Aware |
| 2 | Split | Dim2 <= 0.434 | 0.4571 | 526 | 0.6 | 0.4 | Not Aware |
| 3 | Leaf | Prediction | 0.4213 | 386 | 0.7 | 0.3 | Not Aware |
| 4 | Leaf | Prediction | 0.4666 | 140 | 0.4 | 0.6 | Aware |
| 5 | Split | B6 <= 5.500 | 0.4881 | 3370 | 0.4 | 0.6 | Aware |
| 6 | Leaf | Prediction | 0.4994 | 1865 | 0.5 | 0.5 | Aware |
| 7 | Leaf | Prediction | 0.4417 | 1505 | 0.3 | 0.7 | Aware |
| 8 | Split | Dim2 <= 0.830 | 0.4557 | 604 | 0.6 | 0.4 | Not Aware |
| 9 | Split | B6 <= 2.500 | 0.4979 | 238 | 0.5 | 0.5 | Aware |
| 10 | Leaf | Prediction | 0.4858 | 122 | 0.6 | 0.4 | Not Aware |
| 11 | Leaf | Prediction | 0.3848 | 116 | 0.3 | 0.7 | Aware |
| 12 | Split | Dim2 <= 1.212 | 0.4063 | 366 | 0.7 | 0.3 | Not Aware |
| 13 | Leaf | Prediction | 0.2882 | 89 | 0.8 | 0.2 | Not Aware |
| 14 | Leaf | Prediction | 0.4519 | 277 | 0.7 | 0.3 | Not Aware |