CSIS Evidence-to-Action Decision Support Prototype

Evidence-to-action recommendations generated from the active dataset and its refreshed analytical outputs, subject to LGU validation.

Decision Rules Summary

Select a row to review its recommendation, supporting evidence, and traceability path.

7 service-area rules
Rank Service Area Service Stage Priority Concern Signal Priority
1 Economic and Investment Promotion 5 cluster segments Awareness Economic and Investment Promotion awareness gap Awareness Gap is the strongest current signal for Organization,accreditation and training of tourism related concessions. (76.7%). 76.7% High
2 Public Works and Infrastructure Home-secure citizens with lower service awareness Awareness Public Works and Infrastructure awareness gap Awareness Gap is the strongest current signal for Information and reading center (70.7%). 70.7% High
3 Support to Education Younger or single citizens facing service-access gaps Availment Support to Education availment gap Availment Gap is the strongest current signal for Alternative Learning System and/or other Special Education Programs (64.8%). 64.8% High
4 Social Welfare Services Home-secure citizens requesting further LGU action Need for Action Social Welfare Services need for action Need for Action is the strongest current signal for Older Persons / Senior Citizens Program (60.7%). 60.7% High
5 Environmental Management Younger or single citizens requesting further LGU action Need for Action Environmental Management need for action Need for Action is the strongest current signal for Solid Waste Management (54.7%). 54.7% High
6 Health Services Younger or single citizens requesting further LGU action Need for Action Health Services need for action Need for Action is the strongest current signal for Free Basic Medicine or Low-Cost Medicine Program (52.4%). 52.4% High
7 Governance and Response Younger or single citizens requesting further LGU action Need for Action Governance and Response need for action Need for Action is the strongest current signal for Traffic Management (51.5%). 51.5% High
Selected Decision
Health Services Cluster 2 Need for Action

Prioritize barangay-level health follow-up for underserved Cluster 2 citizens.

Use targeted outreach and referral tracking where citizens report unmet health-service needs but low actual availment.

42% Strongest current service-gap signal
0.78 How reliably the model separates response groups (0 = weak, 1 = strong)
3 Linked analytical sources: clustering, Decision Tree, and Apriori
Observed concern D11 responses show the strongest need-for-action signal.
Group to review first The selected cluster has the strongest related concern signal.
Proposed next step Validate the finding with the responsible service office and LGU records.
Decision status: AI-assisted recommendation pending human review. The system organizes analytical evidence and proposes a planning response; it does not make or approve policy decisions. Confirm local context, feasibility, and responsible-office approval before implementation.

Traceability

Follow the selected recommendation from CSIS survey evidence to a proposed LGU action, or reverse the flow for an audit.

1 Survey evidence D9 and D11 service-stage responses.
2 Model evidence Cluster summary, Decision Tree metrics, and Apriori pattern.
3 Decision rule High need for action with low availment.
4 Proposed LGU action Barangay-level health follow-up for Cluster 2.
Human validation required. Confirm the proposed action using LGU records, service-office evidence, feasibility review, field context, and stakeholder approval.
Detailed Evidence-to-Action Review Public Works and Infrastructure · 5 indicator records Sankey and evidence audit
Analytics -> Harmonization -> Human Review -> Action

Evidence-to-Action Review

Cross-stage, clustering, Decision Tree, and Apriori results are translated into proposed LGU actions. Recommendations remain subject to local validation, feasibility review, and approval.

Public Works and Infrastructure: Priorities are generated from the active dataset's largest indicator-level service gaps. Segment labels identify the portal group with the strongest related concern signal.
Every decision-support record includes: rank, cross-stage result, clustering result, Decision Tree evidence, repeated service-stage pattern, recommended LGU action, priority, responsible office, evidence audit, and monitoring indicator.
Recurring Decision Tree predictors — exploratory consistency signal: MCA Dim1 · 4/5 models (80%) Highest Educational Attainment · 4/5 models (80%) Source of Drinking Water · 4/5 models (80%) MCA DimMagnitude · 3/5 models (60%) MCA Dim2 · 3/5 models (60%) Source of Information · 3/5 models (60%)
Repeated selection can identify factors for LGU investigation, but it does not overcome weak model discrimination, establish causality, or support individual targeting. Frequencies use available indicator models with at least one non-zero feature.
Cross-Service Benchmark

Predictor Coherence

Checks whether recurring Public Works and Infrastructure predictors also recur across the wider CSIS classification workflow.

Open cross-service benchmark
Predictor Public Works and Infrastructure recurrence Cross-service coverage Cross-Service Coherence
MCA Dim1 4/5 models (80%) 133 models · 7 service areas · 4 stages System-wide coherent
The predictor recurs across at least three service areas and two service stages.
Highest Educational Attainment 4/5 models (80%) 102 models · 7 service areas · 4 stages System-wide coherent
The predictor recurs across at least three service areas and two service stages.
Source of Drinking Water 4/5 models (80%) 102 models · 7 service areas · 4 stages System-wide coherent
The predictor recurs across at least three service areas and two service stages.
MCA DimMagnitude 3/5 models (60%) 118 models · 7 service areas · 4 stages System-wide coherent
The predictor recurs across at least three service areas and two service stages.
MCA Dim2 3/5 models (60%) 143 models · 7 service areas · 4 stages System-wide coherent
The predictor recurs across at least three service areas and two service stages.
Source of Information 3/5 models (60%) 119 models · 7 service areas · 4 stages System-wide coherent
The predictor recurs across at least three service areas and two service stages.
Cluster 4 2/5 models (40%) 19 models · 6 service areas · 4 stages System-wide coherent
The predictor recurs across at least three service areas and two service stages.
Age Group 2/5 models (40%) 72 models · 6 service areas · 4 stages System-wide coherent
The predictor recurs across at least three service areas and two service stages.
Source of Electricty 2/5 models (40%) 31 models · 5 service areas · 4 stages System-wide coherent
The predictor recurs across at least three service areas and two service stages.
4Ps Beneficiary 2/5 models (40%) 55 models · 7 service areas · 4 stages System-wide coherent
The predictor recurs across at least three service areas and two service stages.
Relationship to HH Head 2/5 models (40%) 71 models · 7 service areas · 4 stages System-wide coherent
The predictor recurs across at least three service areas and two service stages.
Cluster 0 2/5 models (40%) 22 models · 6 service areas · 4 stages System-wide coherent
The predictor recurs across at least three service areas and two service stages.
Cluster 3 2/5 models (40%) 23 models · 7 service areas · 4 stages System-wide coherent
The predictor recurs across at least three service areas and two service stages.
System-wide coherent means recurrence across at least three service areas and two service stages. Feature importance is unsigned, so this comparison checks recurrence and coverage—not relationship direction, causality, or suitability for individual targeting.
Dynamic indicator flow

Information and reading center

Hover over the Sankey nodes to audit how the four analytical evidence sources converge into an integrated interpretation, priority, proposed action, and monitoring measure.
Rank Cross-Stage Result Clustering Result Decision Tree Evidence Repeated Service-Stage Pattern Recommended LGU Action Priority Implementation and Monitoring
1 Information and reading center
Awareness Gap (70.7%)
Citizens may not yet know enough about this service.
Awareness 29.3%; availment 50.0%; satisfaction 93.3%; need for action 48.1%.
 Married women outside school or reported work (Segment E) (26.1%)
Married women outside school or reported work (Segment E) has the lowest positive response at 26.1% for this indicator.
 Model quality
Needs more predictors: F1 40.7%, ROC AUC 55.0%. The model has difficulty separating respondents based on ROC AUC, recall, or F1.
Leading feature (exploratory)
Highest Educational Attainment (15.8% importance)
Treat as a validation cue, not a reliable explanation or causal factor.
 Respondents who availed the public_works had a 80% likelihood of also were aware of the public_works, and were satisfied with the public_works (Lift: 1.02). Support 74.5%, confidence 80.2%, lift 1.02. Strengthen information campaigns, barangay-level announcements, and direct citizen orientation. High Engineering Office / Planning and Development Office
Monitor: Increase in awareness percentage
2 Barangay roads
Need for Action (62.0%)
Citizens who used the service still indicate a need for government action.
Awareness 93.9%; availment 93.0%; satisfaction 77.0%; need for action 62.0%.
 Married locally working men (Segment D) (65.6%)
Married locally working men (Segment D) has the highest concern signal at 65.6% for this indicator.
 Model quality
Needs more predictors: F1 71.4%, ROC AUC 50.1%. The model has difficulty separating respondents based on ROC AUC, recall, or F1.
Leading feature (exploratory)
Highest Educational Attainment (34.2% importance)
Treat as a validation cue, not a reliable explanation or causal factor.
 Respondents who were aware of the public_works, and felt a need for further action, and were satisfied with the public_works had a 95% likelihood of also availed the public_works (Lift: 1.02). Support 34.9%, confidence 95.3%, lift 1.02. Prioritize follow-up validation with the service office and compare findings with LGU service records. Critical Engineering Office / Planning and Development Office
Monitor: Reduction in need-for-action percentage
3 Flood Control Management System
Need for Action (58.2%)
Citizens who used the service still indicate a need for government action.
Awareness 65.8%; availment 76.8%; satisfaction 82.4%; need for action 58.2%.
 Young single student citizens (Segment B) (62.8%)
Young single student citizens (Segment B) has the highest concern signal at 62.8% for this indicator.
 Model quality
Needs more predictors: F1 56.8%, ROC AUC 51.7%. The model has difficulty separating respondents based on ROC AUC, recall, or F1.
Leading feature (exploratory)
Age Group (21.1% importance)
Treat as a validation cue, not a reliable explanation or causal factor.
 Respondents who were aware of the public_works, and felt a need for further action, and were satisfied with the public_works had a 95% likelihood of also availed the public_works (Lift: 1.02). Support 34.9%, confidence 95.3%, lift 1.02. Prioritize follow-up validation with the service office and compare findings with LGU service records. Critical Engineering Office / Planning and Development Office
Monitor: Reduction in need-for-action percentage
4 Public markets and satellite markets
Need for Action (56.5%)
Citizens who used the service still indicate a need for government action.
Awareness 76.1%; availment 86.5%; satisfaction 84.1%; need for action 56.5%.
 Young single student citizens (Segment B) (60.7%)
Young single student citizens (Segment B) has the highest concern signal at 60.7% for this indicator.
 Model quality
Needs more predictors: F1 51.8%, ROC AUC 51.5%. The model has difficulty separating respondents based on ROC AUC, recall, or F1.
Leading feature (exploratory)
MCA DimMagnitude (37.2% importance)
Treat as a validation cue, not a reliable explanation or causal factor.
 Respondents who were aware of the public_works, and felt a need for further action, and were satisfied with the public_works had a 95% likelihood of also availed the public_works (Lift: 1.02). Support 34.9%, confidence 95.3%, lift 1.02. Prioritize follow-up validation with the service office and compare findings with LGU service records. Critical Engineering Office / Planning and Development Office
Monitor: Reduction in need-for-action percentage
5 Public Cemetery
Need for Action (56.4%)
Citizens who used the service still indicate a need for government action.
Awareness 85.7%; availment 67.0%; satisfaction 82.5%; need for action 56.4%.
 Young single student citizens (Segment B) (59.9%)
Young single student citizens (Segment B) has the highest concern signal at 59.9% for this indicator.
 Model quality
Needs more predictors: F1 44.0%, ROC AUC 55.2%. The model has difficulty separating respondents based on ROC AUC, recall, or F1.
Leading feature (exploratory)
MCA Dim1 (31.8% importance)
Treat as a validation cue, not a reliable explanation or causal factor.
 Respondents who were aware of the public_works, and felt a need for further action, and were satisfied with the public_works had a 95% likelihood of also availed the public_works (Lift: 1.02). Support 34.9%, confidence 95.3%, lift 1.02. Prioritize follow-up validation with the service office and compare findings with LGU service records. Critical Engineering Office / Planning and Development Office
Monitor: Reduction in need-for-action percentage
Priority method: Critical for satisfaction or need-for-action gaps of at least 50%; High for gaps of at least 40%; Medium for gaps of at least 20%; otherwise Low. Recommendations are evidence-based prompts—not automatic policy decisions.