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Ridge regression algorithm based non-invasive anaemia screening using conjunctiva images

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Abstract

Anaemia is one of the most prevalent nutritional deficiency disorders in the world that affects 1.62 billion people of all age groups. Its effects are more significant in the preschool-age children and pregnant women. The objective of this work is to develop a noninvasive method for identifying the anaemic status of a person by estimating their haemoglobin (Hb) level. Data collected from 135 participants is used for developing this model in which 80% of them (108 participants) were classified as a training group and the rest (27 participants) were grouped into a test group and their data is used for testing the model that’s based on the Ridge Regression algorithm. Haemoglobin level of a person is predicted from their digital image of the lower palpebral conjunctiva and basic details like age, sex, height, weight and BMI. These predicted values are closer to the values measured by the standard invasive methods and the Pearson correlation coefficients between the measured haemoglobin value and the predicted haemoglobin value are 0.722 and 0.705 for training and testing respectively. This model is trained to perform well with any smartphone in almost any non-ideal lighting condition without the usage of any external hardware. A web application and mobile application both called 'Chromanalysis' were developed and made available to anyone for screening their anaemic condition with their conjunctiva image and other basic details. This application will help users to assess their haemoglobin levels frequently without undergoing invasive procedures.

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Availability of data and material

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to the containing information that could compromise the privacy of research participants.

Code availability

The proposed model was implemented as a web application and can be accessed through the link. (https://www.chromanalysis.com).

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Acknowledgements

The authors would like to sincerely thank Institutional Review Board (Ethics Committee) of Vision Research Foundation, Sankara Nethralaya, Chennai, India for the study approval and the support provided for data collection. The authors would like to thank the management, teaching and non teaching staffs of ECE Department of KCG College of Technology, Chennai, India for their support to carry out this work. Special thanks to Mr. K. Sivaramakrishnan and Mr. R. Mohanesh Babu for their technical support in developing the mobile and web applications. The authors wish to express their deepest gratitude to the study participants for their excellent cooperation during data collection.

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This work received no external funding.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, KCG College of Technology, Chennai, 600097, India

    Sivachandar Kasiviswanathan & Thulasi Bai Vijayan

  2. Tele-Ophthalmology Department, Sankara Nethralaya, Chennai, 600006, India

    Sheila John

Authors
  1. Sivachandar Kasiviswanathan
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  2. Thulasi Bai Vijayan
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  3. Sheila John
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Contributions

First author and Second author designed the model and the computational framework and analysed the data. First author carried out the implementation, performed the calculations and wrote the manuscript with input from all authors. Second and Third authors conceived the study and were in charge of overall direction and planning.

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Correspondence to Sivachandar Kasiviswanathan.

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Kasiviswanathan, S., Vijayan, T.B. & John, S. Ridge regression algorithm based non-invasive anaemia screening using conjunctiva images. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-02618-3

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