Predicting Blood Type: Assessing Model Performance with ROC Analysis

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.

References in corpus (5)

• A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes
• Diabetes Prediction and Management Using Machine Learning Approaches
• Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction
• Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition
• Comparative performance of ensemble models in predicting dental provider types: insights from fee-for-service data