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All Journal Scientia Psychiatrica Sriwijaya Journal of Forensic and Medicolegal
Sari Sulistyoningsih
Department of Anatomy, CMHC Research Center, Palembang, Indonesia
Biochemistry, Genetics & Molecular Biology Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Neuroscience Public Health

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Neural Network Versus Stepwise Regression for Forensic Stature Estimation from Percutaneous Tibial Dimensions in South Sumatran Malay Adults Sari Sulistyoningsih; Abu Bakar; Eduardo Michael Perez; Ifah Shandy
Sriwijaya Journal of Forensic and Medicolegal Vol. 3 No. 2 (2025): Sriwijaya Journal of Forensic and Medicolegal
Publisher : Phlox Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjfm.v3i2.248

Abstract

Background. Stature estimation from skeletal elements underpins forensic biological profiling and is decisive in disaster victim identification, yet no validated osteometric standard exists for the South Sumatran Malay population. Objective. To develop population-specific stature-estimation equations from percutaneous tibial dimensions and to compare stepwise multiple linear regression (SMLR) with a multilayer perceptron artificial neural network (MLP-ANN). Methods. 450 healthy South Sumatran Malay adults (225 male, 225 female) aged 20-50 years were enrolled at CMHC Research Center, and reported per STROBE. Five percutaneous right-tibial dimensions were measured by one certified anthropologist under the Martin and Saller convention (all relative technical errors of measurement below 1.5%), and a stratified 70:30 partition withheld 135 observations for testing. SMLR was compared against a grid-search-optimised 5-64-32-16-1 MLP-ANN. Results. Sexual dimorphism was large for every dimension (Cohen's d 1.42-1.75; all p < 0.001). Percutaneous tibial length was the strongest single predictor (males r = 0.812, 95% CI 0.762-0.852). The calibration-corrected pooled regression reached R2 = 0.742 with RMSE ±4.82 cm, whereas the network reached R2 = 0.914 (95% CI 0.891-0.933) and RMSE ±2.78 cm: ΔR2 = 0.172 (bootstrap 95% CI 0.141-0.203), a 23.2% gain in explained variance and a 42.3% reduction in error that narrows the 95% forensic identification window from ±9.45 cm to ±5.45 cm. Conclusion. These first population-specific, artificial-intelligence-driven standards materially improve the biological profiling of incomplete human remains in Indonesian medicolegal and disaster victim identification practice.
Construct Overlap and Threat-Arousal Bridges Across Post-Traumatic Stress, Insomnia, and Anxiety in Ukrainian Adults During War: An Ordinal Network Analysis Brenda Jaleel; Rheina Weisch Fedre; Sari Sulistyoningsih; Anna Melnyk
Scientia Psychiatrica Vol. 7 No. 3 (2026): Scientia Psychiatrica
Publisher : HM Publisher in collaboration with CMHC Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/scipsy.v7i3.215

Abstract

Background. Post-traumatic stress, insomnia, and anxiety often co-occur during armed conflict, but total scores cannot show which symptoms connect the domains. Objective. To estimate an item-level symptom network in Ukrainian adults during war, identify cross-domain bridges, and test their robustness to estimation, overlap, and sample choices. Methods. This secondary cross-sectional analysis used responses from 241 Ukrainian adults recruited online in December 2024. Predefined communities comprised seven Insomnia Severity Index (ISI), seven Generalized Anxiety Disorder-7 (GAD-7), and 20 PTSD Checklist for DSM-5 (PCL-5) items. A rank-based Gaussian-copula matrix was regularized with graphical lasso and the extended Bayesian information criterion (EBIC; γ = 0.50); bridge expected influence quantified cross-community connections. Accuracy was evaluated with 500 fixed-tuning bootstraps, case-dropping, and four sensitivity analyses. Results. Of 561 possible edges, 223 were nonzero, 106 of them cross-scale. PCL-5 sleep difficulty had the largest bridge expected influence (1.059; 95% bootstrap interval 0.871–1.207), followed by GAD-7 fear that something awful might happen (0.682; 0.488–0.888), GAD-7 irritability (0.603), ISI sleep-onset difficulty (0.591), and PCL-5 irritability/aggression (0.566). The strongest cross-scale edges joined parallel sleep items (0.460) and irritability items (0.473). Rankings changed little under polychoric estimation, lower EBIC γ, and N = 242 (Spearman ρ = 0.993–1.000). Without the two overlapping PCL-5 items, concordance remained substantial (ρ = 0.874), and fear, startle, relaxation, concentration, restlessness, and hypervigilance became prominent. Conclusion. Bridges reflected shared item content and a broader threat-arousal pattern. Sleep, fear, irritability, startle, relaxation, and concentration should be assessed together; cross-sectional centrality is not a causal treatment target.
Co-Authors Abu Bakar Anna Melnyk Brenda Jaleel Eduardo Michael Perez Ifah Shandy Rheina Weisch Fedre