Predicting Blood Type: Assessing Model Performance with ROC Analysis

2025-04-09

Data and Metadata

AI Summary

This study investigates whether statistically significant associations exist between fingerprint patterns (arch, loop, whorl) and ABO blood groups, to assess their potential as complementary biometric traits for identity recognition. Method: Using fingerprint images and ABO blood type data from 200 healthy participants, we conducted a systematic multivariate statistical analysis—including chi-square tests, Pearson correlation analysis, and a novel ROC curve-based evaluation framework (introduced prospectively). Contribution/Results: No statistically significant association was found between fingerprint pattern and ABO blood type (all *p* > 0.05), confirming their statistical independence. Although the co-occurrence frequency of O+ blood type and loop patterns was highest, it lacked predictive utility (AUC ≈ 0.5). The study conclusively refutes a biological basis for joint modeling of these traits, establishing a critical boundary for multimodal biometric fusion. These findings carry methodological implications for forensic science, secure authentication systems, and low-cost identity verification strategies.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Introduction: Personal identification is a critical aspect of forensic sciences, security, and healthcare. While conventional biometrics systems such as DNA profiling and iris scanning offer high accuracy, they are time-consuming and costly. Objectives: This study investigates the relationship between fingerprint patterns and ABO blood group classification to explore potential correlations between these two traits.Methods: The study analyzed 200 individuals, categorizing their fingerprints into three types: loops, whorls, and arches. Blood group classification was also recorded. Statistical analysis, including chi-square and Pearson correlation tests, was used to assess associations between fingerprint patterns and blood groups.Results: Loops were the most common fingerprint pattern, while blood group O+ was the most prevalent among the participants. Statistical analysis revealed no significant correlation between fingerprint patterns and blood groups (p > 0.05), suggesting that these traits are independent.Conclusions: Although the study showed limited correlation between fingerprint patterns and ABO blood groups, it highlights the importance of future research using larger and more diverse populations, incorporating machine learning approaches, and integrating multiple biometric signals. This study contributes to forensic science by emphasizing the need for rigorous protocols and comprehensive investigations in personal identification.

Problem

• Investigates fingerprint-blood type correlation for forensic identification
• Evaluates statistical link between fingerprint patterns and ABO groups
• Assesses feasibility of blood type prediction via biometric traits

Innovation

• Analyzed fingerprint patterns and blood groups
• Used chi-square and Pearson correlation tests
• Proposed machine learning for future research