European Journal of Cardiovascular Medicine

Identification is the process of ascertaining an individual's distinctiveness, whether it is in its whole or to some extent. The need to integrate several approaches is underscored for achieving the highest level of precision in identification [1]. The precise origin of fingerprint identification is uncertain. However, evidence indicates that it has been acknowledged for millennia [2]. Multiple techniques are used for identification, including race, sex, age, fingerprints, and DNA profiles [3]. Fingerprints are often regarded as the most dependable means of identification owing to their unparalleled distinctiveness. The probability of two persons having identical fingerprints is relatively low [3]. Even though identical twins have the same genetic makeup, they possess unique fingerprints. Applications of Fingerprints: Fingerprints are essential in several domains, including law enforcement, which uses fingerprint comparisons to identify offenders and fugitives [4]. Assisting in the identification of missing individuals and unidentified dead people [4]. Providing aid in circumstances such as forgetfulness or instances of baby mix-ups [4]. Legal issues include several areas, such as determining paternity or maternity, divorce lawsuits, and worker's compensation claims [5].

The study was conducted on a total of 224 individuals. The study is an observational study involving male and female volunteers is being conducted, focusing on various demographic details of the participants. Variables include fingerprint patterns and blood groupings of studied population recorded. Participants were classified according to their fingerprint patterns. Information on the distribution of males and females within each group was documented.

Criteria for inclusion: Individuals must participate in a state of optimal health without any pre-existing medical issues. Individuals must possess fingerprints that are devoid of any scars, congenital malformations, or irregularities that might hinder the process of fingerprint analysis.

Criteria for exclusion: Individuals who have sustained burns resulting in permanent scars, deformities, or impaired finger mobility will be ineligible. Participants with chronic dermatological conditions that specifically impact the fingers, such as psoriasis or leprosy, will not be included in the study to avoid possible disruption of fingerprint patterns. Individuals who are unwilling or unable to adhere to research instructions will be excluded.

Table 1: The distribution of the study population with various blood groups in this statistical study

Table 1 shows Male and Female are categorized by their blood type, and the number of study subject in each category is recorded. The aggregate number of, including both males and females, categorized by blood type. The proportion of person in each blood type compared to the total number of participants (224). The research includes a greater number of girls (138) compared to men (86). This indicates that the data is disproportionately represented by females, accounting for 61.6% of the total. According to the data, the blood type O+ is the most prevalent, accounting for 73 cases (38.4%).

Table 2: Distribution of different types of fingerprint patterns amongst the males and females

Table 2 presented the distribution of fingerprint patterns among male and females. Below is an analysis of the data and a few preliminary insights: Dermatoglyphic Patterns: Loops, whorls, arches, and composites are the different patterns of fingerprints. The loop fingerprint pattern is the most prevalent among both men (52.3%) and females (53.6%), with a slight female predominance. In general, there is a high degree of similarity in the distribution of fingerprint patterns between men and females. No category displays a noticeable difference more significant than 3%. While Loops are the predominant pattern for both genders, there is a slightly greater prevalence of Loops and Whorls among females, while men have a somewhat higher prevalence of Arches and Composites.

Table 3: Chi-square test

Table 3 shows results of the Chi-Square test, chi-squared statistic is 0.2743, and the p-value is 0.9648. Explanation: Given that the p-value (0.9648) is above the significance level of 0.05, we do not have sufficient evidence to reject the null hypothesis. Consequently, there is an absence of a statistically significant correlation between fingerprint patterns and gender. However, the distribution of fingerprint patterns is comparable between men and females.

Figure 1 shows the percentage distribution of the different types of fingerprint patterns amongst the male and female participants in the survey.

Figure 2 shows the different types of the fingerprints

This research examined the correlation between fingerprint patterns, gender, and blood types. Distribution of Blood Groups: 41.6% of the subjects were classified as belonging to blood group B. The percentages of blood types O and A are almost equal, with each accounting for 23.2%. The blood group AB had the lowest prevalence, accounting for just 12% of the population. Relationship between Fingerprint Patterns and Blood Groups: Loop occurrence: The proportion of loops was most significant among those with blood type B (76.61%) and lowest among those with blood group AB (15.28%). This is consistent with the results reported by Mahajan et al. (1986) and Kshirsagar et al. (2001) [7, 8]. The proportion of whorls was most significant in those with blood type O (65.76%) and lowest in those with blood group AB (11.11%). This is similar to the findings reported by Bhardwaja (200x) and Prateek et al. (200x) [6,9,10]. The proportion of arches was largest in AB, accounting for 29.79% of the total, and lowest in O, representing just 3.45%. This conclusion aligns with prior studies conducted by Mahajan et al. (1986) and Kshirsagar et al. (2001) [7, 8]. Relationship between Fingerprint Patterns and Gender: Consistent with the findings of Prateek et al. (200x), this research observed a greater prevalence of loops in females (63.68%) in comparison to men (54.4%) [9]. Sharma et al. (200x) found that there was a more significant occurrence of loops in females with blood type B (63.68%) compared to men (35.84%) [11]. In contrast, men had a more significant occurrence of whorls (54.4%) in blood type O, whereas females had a lower frequency (25.92%). This is analogous to the study conducted by Sharma et al. in the year 200x [11]. The proportion of arches was almost the same for both genders in blood types A and AB [12].

This research examined the correlation between fingerprint patterns, gender, and blood types among students enrolled at Al-Kindy College of Medicine in Baghdad, Iraq. The objective was to tackle the need for more previous studies on this subject among this particular demographic. The discoveries might potentially enhance techniques for determining sex and blood type via the study of fingerprints. The prevalence of blood groups differed between men and females, with B+ being the most common blood type in males and O+ being the most common blood group in females. This is consistent with research done in Navi Mumbai [13]. A noteworthy correlation between gender and blood group was detected (p < 0.05), in contrast to a study conducted at Delta State University, Nigeria, which did not find any such correlation (p > 0.05) [14]. Fingerprint Patterns: Loops were the most common fingerprint type for both males and females, followed by whorls and arches. This is consistent with the results of research conducted at Malabar Medical College [15]. There was no significant correlation between gender and fingerprint pattern (p > 0.05), which aligns with the findings of Eboh et al. and Odokuma et al. [14, 17]. Fingerprint Patterns and Blood Groups: Irrespective of blood type (ABO or Rhesus), loops were the predominant fingerprint pattern, followed by whorls and arches. This is consistent with the findings presented by Eboh et al. and Prateek et al. [14, 16]. The study did not find a statistically significant correlation between fingerprint patterns and ABO blood type (p > 0.05), which aligns with the findings of previous studies conducted by Eboh et al., Odokuma et al., and Kshirsagar et al. [14, 17, 18]. Nevertheless, this discovery is in direct opposition to previous research conducted by Bharadwaja and Mehta [19,13]. Rhesus Blood type: This research found no statistically significant correlation between fingerprint pattern and Rhesus blood type (p > 0.05). In contrast, the research conducted by Delta State University in Nigeria discovered a significant correlation [14]. In summary, this research enhances our comprehension of fingerprint patterns, gender, and blood types among Iraqi students. While several discoveries are consistent with other studies, others emphasize the need for more inquiry and possible variances particular to specific populations.

Fingerprints have become a helpful asset in several areas owing to their distinct and recognizable features. Below is an analysis of current research that investigates the possible uses of this technology: A research presents an innovative method for determining blood groups by analyzing fingerprints [20]. This approach utilizes fingerprint minutiae characteristics and employs machine learning methods, namely Multiple Linear Regression with Ordinary Least Squares, to forecast blood types with a precision rate of 62% [20]. The authors propose that future studies include a more extensive sample size and investigate other fingerprint characteristics in order to enhance accuracy [20]. A separate research study examines the feasibility of fingerprint analysis in determining blood types while also exploring possible correlations with age-related or lifestyle disorders such as hypertension, diabetes, and arthritis [21]. This study investigates the relationship between fingerprint patterns, blood types, and individual age, with the goal of comprehending any associations with health issues [21]. Another research introduces a complete approach to fingerprint recognition and identification that relies on intricate characteristics [22]. The procedure involves initial noise reduction and picture improvement, followed by feature extraction that specifically targets fingerprint ends and bifurcations. Ultimately, a matching step employs Euclidean distance to evaluate the similarity of fingerprint pictures [22]. A separate research endeavor investigates the use of fingerprint sensors and approaches for extracting features in order to analyze fingerprints [23]. The primary objective of this study is to adequately describe fingerprint patterns by extracting characteristics such as routing information, BGP descriptors, and GaborHoG descriptors [23]. According to the research mentioned in reference [24], there is a clear and positive relationship between fingerprint patterns and ABO blood types. The authors highlight the ongoing progress in fingerprint technology and the creation of increasingly efficient and precise fingerprint-matching algorithms. These developments provide a substantial contribution to the area of automated identification [24].

In general, fingerprint analysis shows great promise for a wide range of applications beyond its usual use for identification.  This study discovered a more significant occurrence of loops and whorls in both female and male subjects. Curiously, a prior investigation conducted on medical students in Mangalore revealed a contrasting pattern. Females had a higher prevalence of loops and arches, whilst males displayed a more significant occurrence of whorls [27]. Nevertheless, our study, like other research, failed to establish a statistically significant correlation between gender and fingerprint patterns (p > 0.05) [30, 31].  Irrespective of blood type (ABO or Rhesus), loops were the predominant fingerprint pattern, followed by whorls and arches. This is consistent with the results of prior investigations [25, 26,27,30]. Remarkably, the prevalence of loop, whorl, and arch patterns exhibited a consistent distribution among persons with various ABO-Rhesus blood groups (A+, B+, B-, AB+, O+, and O-). The findings align with the research conducted by Bharadwaja et al., Prateek and Keerthi, and Dennis Eboh [27, 30]. Nevertheless, certain inconsistencies were observed. According to our study, loops were shown to be the most frequently occurring pattern in individuals with blood type O-. However, Bharadwaja et al. and Dennis Eboh observed that whorls were the predominant pattern in individuals with blood type A- and loops in individuals with blood type O-, respectively [30]. In addition, the AB- -people in their investigations exhibited similar proportions of all three fingerprint patterns. Conflicting findings about the relationship between ABO blood group and fingerprint patterns have been observed. This study demonstrated a statistically significant correlation between ABO blood group and fingerprint patterns (p < 0.05), consistent with prior findings from other researchers [25, 27]. Nevertheless, the research conducted by Kshirsagar et al., Dennis Eboh, and Odokuma et al. did not uncover any evidence to support this connection [26, 30, 31].

This study investigated the correlations among fingerprint patterns, blood types, and gender in a sample of 224 persons. Although O+ was the most common blood group, the study did not find any statistically significant correlation between fingerprint patterns and blood groups, regardless of gender. It is worth mentioning that loop patterns were the predominant type of fingerprint for both males and females, indicating a significant level of similarity in the distribution of fingerprints between genders.

1. Vij K. Identification: Text Book of Forensic Medicine and Toxicology Principles and practice. 3rd ed. New Delhi: Elsevier; 2005. p. 50.
2. Galloway V, Charlton D. Fingerprints: Forensic Human Identification – An Introduction. Boca Raton: CRC Press; 2006. p. 57.
3. Subrahmanyam BV, editor. Personal Identity: Modi’s Medical Jurisprudence and Toxicology. 22nd ed. New Delhi: Butterworths Tripathi Publication; 1999. p. 39, 40, 72.
4. Khadri SY. Study of Fingerprint Patterns and Gender Distribution in and Around Bijapur. Bangalore: Rajiv Gandhi University and Health Sciences; 2011 Apr. p. 1-2.
5. Nandy A. Identification: Principles of Forensic Medicine including Toxicology. 3rd ed. Kolkata: New Central Book Agency Pvt Ltd; 2010. p. 158, 189-190, 202.
6. Bhardwaja A,Sarswat P.K.,Agrawal S.K., Banerji P. and Bharadwaj S. Pattern of fingerprints in different ABO blood groups Journal of Forensic medicine & toxicology ,2004;21(2):49-52.
7. Mahajan AA.Dermatoglyphics and ABO Blood group. Thesis submitted for MS Anatomy, Aurangabad, 1986.
8. Kashirsagar SV,etal, study of fingerprint patterns in ABO Blood group ,J AnatSoc India,2003;52 (1):82-115.
9. Prateek rastogi A study of fingerprint in relation to gender and blood group J Indian Acade Forensic Med,32(1) .
10. Gowda mst and Rao CP.A study to evaluate relationship between dermatoglyphics feature and blood groups .J Anat, society of ind.1996, 45:39.
11. Sharma P R, Gautam ,A.K.Tiwari P.K dermatoglyphics variations in five ethnogeographical cohorts of Indian populations; A Pilot study. the journal of biological Anthropology.2008;(1):57-66.
12. Singh DB. Role of Finger Print Pattern in Relationship with Blood Group and Gender. Journal of Medical Science And clinical Research. 2016 Mar 3;4(3).
13. Mehta AA, Mehta AA. Palmar dermatoglyphics in ABO, RH blood groups. Int J Biol Med Res. 2011;2(4):961-4.
14. Eboh D. Fingerprint patterns in relation to gender and blood group among students of Delta State University, Abraka, Nigeria. J Exp Clin Anat. 2013;12(2):82.
15. Salmani D, Purushothaman S, Gopalakrishna, Ravindran L, Nath S, Pushkar B. A study of dermatoglyphics in relation with blood groups among first year MBBS students in Malabar Medical College. Indian J Clin Anat Physiol. 2016;3(3):348.
16. Prateek R, Keerthi RP. A study of fingerprints in relation to gender and blood. J Indian Acad Forensic Med. 2010;32(1):11-4.
17. Odokuma EI, Igbigbi PS, Emundianughe TS. A study of thumb print patterns and ABO blood group distribution. J Exp Clin Anat. 2008;7(1):22-6. [Accessed 27th April 2019].
18. Kshirsagar SV, Burgul SN, Kamkhedkar SG. A study of fingerprint patterns in ABO blood group. J Anat Soc India. 2003;52(1):82-115.
19. Bharadwaja A, Saraswat PK, Aggarwal SK, Banerji P, Bharadwaja S. Pattern of fingerprints in different ABO blood groups. J Indian Acad Forensic Med. 2004;26(1):6-9.
20. Vijaykumar, Patil N, Ingle DR. A novel approach to predict blood group using fingerprint map reading. 2021 6th International Conference for Convergence in Technology (I2CT). IEEE; 2021.
21. Patil V, Ingle DR. An association between fingerprint patterns with blood group and lifestyle-based diseases: a review. Artif Intell Rev. 2021;54:1803-39.
22. Ali MMH, Osman W, Nordin MJ, Elfaki AO, Mashohor S, Sid-Ahmed MA. Fingerprint recognition for person identification and verification based on minutiae matching. 2016 IEEE 6th International Conference on Advanced Computing (IACC). IEEE; 2016.
23. Alshehri H, Muhammad G, Bebis G, Mirza AM, Rida S. Cross-sensor fingerprint matching method based on orientation, gradient, and Gabor-HOG descriptors with score level fusion. IEEE Access. 2018;6:28951-68.
24. Fayrouz I, Noor Eldin NF, Irshad AH. Relation between fingerprints and different blood groups. J Forensic Leg Med. 2012;19(1):18-21.
25. Mehta AA, Mehta AA. Palmar dermatoglyphics in ABO, RH blood groups. Int J Biol Med Res. 2011;2(4):961-4.
26. Kshirsagar SV, Burgul SN, Kamkhedkar SG, Maharashtra A. Study of fingerprint patterns in ABO blood group. J Anat Soc India. 2003;52(1):82-115.
27. Prateek R, Keerthi RP. A study of fingerprints in relation to gender and blood. J Indian Acad Forensic Med. 2010;32(1):11-4.
28. Raolti SK, Shah KA, Patel VC, Menat AK, Mori RN, Chaudhari NK. An effort to determine blood groups and gender from fingerprints. Natl J Community Med. 2013;4(1):158-60.
29. Radhika RH. Pattern of fingerprints and their relation with blood groups. Indian J Basic Appl Med Res. 2016;5(2):744-9.
30. Eboh DE. Fingerprint patterns in relation to gender and blood group among students of Delta State University, Abraka, Nigeria. J Exp Clin Anat. 2013;12(2):81-6.
31. Odokuma EI, Igbigbi PS, Emundianughe TS. A study of thumb print patterns and ABO blood group distribution. J Exp Clin Anat. 2008;7(1):22-6.