In the current era of the digital world, the hash of any digital means considered as a footprint or fingerprint of any digital term but from the ancient era, human fingerprint considered as the most trustworthy criteria for identification and it also cannot be changed with time even up to the death of an individual. In the court of law, fingerprint-proof is undeniably the most dependable and acceptable evidence to date. Fingerprint designs are exclusive in each human and the chance of two individuals having identical fingerprints is an exceptional case about one in sixty-four thousand million also the fingerprint minutiae patterns of the undistinguishable twins are different, and the ridge pattern of each fingertip remain unchanged from birth to till death. Fingerprints can be divided into basic four categories i.e. Loop, whorl, arch, and composites, nevertheless, there are more than 100 interleaved ridge and valleys physiognomies, called Galton’s details, in a single rolled fingerprint. Due to the immense potential of fingerprints as an effective method of identification, the present research paper tries to investigate the problem of blood group identification and analysis of diseases those arises with aging like hypertension, type 2-diabetes and arthritis from a fingerprint by analyzing their patterns correlation with blood group and age of an individual. The work has been driven by studies of anthropometry, biometric trademark, and pattern recognition proposing that it is possible to predict blood group using fingerprint map reading. Dermatoglyphics as a diagnostic aid used from ancient eras and now it is well established in number of diseases which have strong hereditary basis and is employed as a method for screening for abnormal anomalies. Apart from its use in predicting the diagnosis of disease; dermatoglyphics is also used in forensic medicine in individual identification, physical anthropology, human genetics and medicine. However, the Machine and Deep Learning techniques, if used for fingerprint minutiae patterns to be trained by Neural Network for blood group prediction and classification of common clinical diseases arises with aging based on lifestyle would be an unusual research work.

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指纹模式与血型和生活方式疾病之间的关联：综述

在当今的数字世界时代，任何数字手段的哈希值都被认为是任何数字术语的足迹或指纹，但从远古时代开始，人的指纹就被认为是最值得信赖的识别标准，它也不能随着时间而改变直到一个人的死亡。在法庭上，指纹证明无疑是迄今为止最可靠和可接受的证据。指纹设计在每个人身上都是独一无二的，两个人拥有相同指纹的几率是一个例外，大约六万四千万分之一，而且无法区分的双胞胎的指纹细节图案也不同，每个指尖的脊纹图案保持不变生至死。指纹可以分为基本的四类，即环形、螺纹、拱形、和复合材料，然而，有超过 100 个交错的脊和谷面相，称为高尔顿的细节，在一个单一的滚动指纹中。由于指纹作为一种有效的识别方法的巨大潜力，本研究论文试图通过分析指纹来研究血型识别和分析随着年龄增长而引起的疾病，如高血压、2型糖尿病和关节炎。模式与个体的血型和年龄相关。这项工作是由人体测量学、生物特征商标和模式识别的研究推动的，这些研究表明可以使用指纹图读取来预测血型。皮纹学作为一种自古以来就使用的诊断辅助手段，现在已在许多具有很强遗传基础的疾病中得到广泛应用，并被用作筛查异常异常的方法。除了用于预测疾病的诊断；dermatoglyphics 还用于法医学中的个体识别、体质人类学、人类遗传学和医学。然而，机器和深度学习技术，如果用于指纹细节模式，由神经网络训练，用于血型预测和基于生活方式的衰老出现的常见临床疾病的分类，将是一项不同寻常的研究工作。dermatoglyphics 还用于法医学中的个体识别、体质人类学、人类遗传学和医学。然而，机器和深度学习技术，如果用于指纹细节模式，由神经网络训练，用于血型预测和基于生活方式的衰老出现的常见临床疾病的分类，将是一项不同寻常的研究工作。dermatoglyphics 还用于法医学中的个体识别、体质人类学、人类遗传学和医学。然而，机器和深度学习技术，如果用于指纹细节模式，由神经网络训练，用于血型预测和基于生活方式的衰老出现的常见临床疾病的分类，将是一项不寻常的研究工作。