Abstract
Primary health institution as the main health care institution that addresses the health challenges of individuals in rural areas and those with limited financial capacity in Nigeria houses quality information. However, this information is not properly managed and is not easily available for patients and health personnel. This is because the traditional filing system is still prevalently used and in cases where databases are used, access to a computer system is still limited. With the popularity of mobile devices, an avenue for easy and quick access to medical records presents itself. However, this also gives rise to record security issues. Taking advantage of the fingerprint scanner on most of these devices, this paper presents the development of a mobile primary health care system - PriHealth. The system uses fingerprint authentication to secure access to the back-end of the system which is the database that holds medical records. The accuracy of the approach showed an encouraging result of 97%.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsChange history
17 November 2022
In the originally published version of chapter 34 the affiliations of two Authors were indicated incorrectly. This has been corrected as follows: affiliation of the Author Roseline Oluwaseun Ogundokun has been changed to “Department of Computer Science, Landmark University, Omu Aran, Nigeria”; affiliation of the Author Sanjay Misra has been changed to “Department of Computer Science and Communication, Ostfold University College, Halden, Norway”.
References
World Health Organization (WHO), Primary Health Care. https://www.who.int/news-room/fact-sheets/detail/primary-health-care. Accessed 2 July 2021
Awotunde, J.B., Ogundokun, R.O., Misra, S.: Cloud and IoMT-based big data analytics system during COVID-19 pandemic. Internet Things 2021, 181–201 (2021)
Wendl, U., Wyczisk, H.: Time Management System for Medical Applications, particularly in a Hospital Setting (2011). https://patents.google.com/patent/US8046239. Accessed 11 May 2020
Mogli, G.D.: Role of Biometrics in healthcare privacy and security management system. Sri Lanka J. Bio-Med. Inform. 2(4), 156–165 (2011)
Mesmoudi, S., Feham, M.: Bsk-wbsn: biometric symmetric keys to secure wireless body sensors networks. Int. J. Network Secur. Appl. (IJNSA) 3(5), 155–166 (2011)
Adeniyi, E.A., Ogundokun, R.O., Awotunde, J.B.: IoMT-based wearable body sensors network healthcare monitoring system. Studies in Computational Intelligence 2021(933), 103–121 (2021)
Ikhu-Omoregbe, N.A., Azeta, A.A.: A voice-based mobile prescription application for healthcare services (VBMOPA). Int. J. Electr. Comput. Sci. IJECS-IJENS 10(02), 73–78 (2010)
Abayomi-Alli, A., Ikuomola, A., Aliyu, O., Abayomi-Alli, O.: Development of a Mobile Remote Health Monitoring system–MRHMS. African J. Comput. ICT, 14–22 (2014)
Azeta, A.A., et al.: Preserving patient records with biometrics identification in e-Health systems. In Data, Engineering and Applications, pp. 181–191. Springer, Singapore (2019)
Jhaveri, H., Sanghavi, D.: Biometric security system and its applications in healthcare. International Journal of Technology (2014
Shakil, K., Zareen, F., Alam, M., Jabin, S.: BAMHealthCloud: a biometric authentication and data management system for healthcare data in cloud. J. King Saud Univ. Comput. Inf. Sci. 32 (2017). https://doi.org/10.1016/j.jksuci.2017.07.001
Zhao, Y., Liu, L., Qi, Y., Lou, F., Zhang, J., Ma, W.: Evaluation and design of public health information management system for primary health care units based on medical and health information. J. Infect. Public Health (2019). https://doi.org/10.1016/j.jiph.2019.11.004
Chung, K., Park, R.C.: P2P-based open health cloud for medicine management. Peer-to-Peer Networking Appl. 13(2), 610–622 (2019). https://doi.org/10.1007/s12083-019-00791-7
Chen, J., Zhang, L., Ackah-Arthur, H., Omari, M., Xi, J.: An architecture of urban regional health information system and its data conversion algorithm. SpaCCS Workshops 2017, 339–349 (2017)
Al Omar, A., Rahman, M.S., Basu, A., Kiyomoto, S.: MediBchain: a blockchain based privacy preserving platform for healthcare data. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, K.-K. (eds.) SpaCCS 2017. LNCS, vol. 10658, pp. 534–543. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-72395-2_49
Hoang, N.: Detection of surface crack in building structures using image processing technique with an improved otsu method for image thresholding. Advances in Civil Engineering (2018). Doi: https://doi.org/10.1155/2018/3924120
Kang, S., Iwana, B.K., Uchida, S.: Cascading modular U-Nets for document image binarization. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 675–680 (2019). doi:https://doi.org/10.1109/ICDAR.2019.00113
Suthar, S.B., Goradia, R.S., Dalwadi, B.N., Patel, S.M., Patel, S.: Performance scrutiny of thinning algorithms on printed gujarati characters and handwritten numerals. In: Mishra, D., Nayak, M., Joshi, A. (eds.) Information and Communication Technology for Sustainable Development. Lecture Notes in Networks and Systems. 9. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-3932-4_27
Bolelli, F., Grana, C.: Improving the performance of thinning algorithms with directed rooted acyclic graphs. In: Ricci, E., Rota Bulò, S., Snoek, C., Lanz, O., Messelodi, S., Sebe, N. (eds.) ICIAP 2019. LNCS, vol. 11752, pp. 148–158. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30645-8_14
Bai, X., Ye, L., Zhu, J., Zhu, L., Komura, T.: Skeleton filter: a self-symmetric filter for skeletonization in noisy text images. IEEE Trans. Image Process. 29(1815–1826), 2020 (2020). https://doi.org/10.1109/TIP.2019.2944560
Gramblička, M., Vasky, J.: Comparison of Thinning Algorithms for Vectorization of Engineering Drawings. Journal of Theoretical and Applied Information Technology, 94(2) (2016)
Patil, T., Nandusekar, S.: Different techniques used in the process of feature extraction from fingerprint. Int. J. Innov. Eng. Res. Technol. (IJIERT) 6(9) 2019
Zhi, H., Liu, S.: Face recognition based on genetic algorithm. J. Vis. Commun. Image R. 58, 495–502 (2019). https://doi.org/10.1016/j.jvcir.2018.12.012
Mirjalili, S., Song Dong, J., Sadiq, A.S., Faris, H.: Genetic algorithm: theory, literature review, and application in image reconstruction. In: Mirjalili, S., Song Dong, J., Lewis, A. (eds.) Nature-Inspired Optimizers. SCI, vol. 811, pp. 69–85. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-12127-3_5
Ahmed, B.T., Abdulhameed, O.Y.: Fingerprint recognition based on shark smell optimization and genetic algorithm. Int. J. Adv. Intell. Inform. 6(2), 123–134 (2020). https://doi.org/10.26555/ijain.v6i2.502
Singh, R.K., Panchal, V.K., Singh, B.K.: A review on genetic algorithm and its applications. In: 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), 2018, p. 376–380 (2018). https://doi.org/10.1109/ICGCIoT.2018.8753030
Sagayam, G.M., Ponraj, D.N., Winston, J., Yaspy, J.C., Jeba, D.E., Clara, A.: Authentication of biometric system using fingerprint recognition with euclidean distance and neural network classifier. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 8(4) (2019)
Virdaus, I.K., Mallak, A., Lee, S.-W., Ha, G., Kang, M.: Fingerprint Verification with Crossing Number Extraction and Orientation-Based Matching, Research Gate (2017)
Toudjeu, I.T., Tapamo, J.-R.: Circular Derivative Local Binary Pattern Feature Description for Facial Expression Recognition, Advances in Electrical and Computer Engineering, 19(1) (2019)
Wang, J., Fan, Y., Li, N.: Dominant color and texture feature extraction for banknote discrimination. J. Electron. Imaging 26(4) (2017). doi: 10.1117/1. JEI.2 6.4.043011
Nishom, M.: Perbandingan Akurasi Euclidean Distance, Minkowski Distance, dan Manhattan Distance pada Algoritma K-Means Clustering berbasis Chi-Square, Jurnal Informatika: Jurnal Pengembangan IT (JPIT) 4(01) (2019). https://doi.org/10.30591/jpit.v4i1.1253
Religia, Y., Sunge, A.S.: Comparison of distance methods in K-Means algorithm for determining village status in Bekasi District. In: 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), pp. 270–276 (2019). https://doi.org/10.1109/ICAIIT.2019.8834604
Ismail, Z.H., Chun, A.K.K., Razak, M.I.S.: Efficient herd – outlier detection in livestock monitoring system based on density – based spatial clustering. In: IEEE Access, vol. 7, pp. 175062–175070 (2019). https://doi.org/10.1109/ACCESS.2019.2952912
Ogundokun, R.O., Awotunde, J.B., Misra, S., Umoru, D.O.: Drug verification system using quick response code. Commun. Comput. Inf. Sci. 1350, 535–545 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Adeniyi, J.K., Adeniyi, T.T., Ogundokun, R.O., Misra, S., Agrawal, A., Ahuja, R. (2022). PriHealth: A Fingerprint-Based Mobile Primary Healthcare Management System. In: Mukhopadhyay, S., Sarkar, S., Dutta, P., Mandal, J.K., Roy, S. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2022. Communications in Computer and Information Science, vol 1579. Springer, Cham. https://doi.org/10.1007/978-3-031-10766-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-031-10766-5_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-10765-8
Online ISBN: 978-3-031-10766-5
eBook Packages: Computer ScienceComputer Science (R0)