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Medical Diagnosis Using Adaptive Perceptive Particle Swarm Optimization and Its Hardware Realization using Field Programmable Gate Array

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Abstract

The paper proposes to develop a field programmable gate array (FPGA) based low cost, low power and high speed novel diagnostic system that can detect in absence of the physician the approaching critical condition of a patient at an early stage and is thus suitable for diagnosis of patients in the rural areas of developing countries where availability of physicians and availability of power is really scarce. The diagnostic system could be installed in health care centres of rural areas where patients can register themselves for periodic diagnoses and thereby detect potential health hazards at an early stage. Multiple pathophysiological parameters with different weights are involved in diagnosing a particular disease. A novel variation of particle swarm optimization called as adaptive perceptive particle swarm optimization has been proposed to determine the optimal weights of these pathophysiological parameters for a more accurate diagnosis. The FPGA based smart system has been applied for early detection of renal criticality of patients. For renal diagnosis, body mass index, glucose, urea, creatinine, systolic and diastolic blood pressures have been considered as pathophysiological parameters. The detection of approaching critical condition of a patient by the instrument has also been validated with the standard Cockford Gault Equation to verify whether the patient is really approaching a critical condition or not. Using Bayesian analysis on the population of 80 patients under study an accuracy of up to 97.5% in renal diagnosis has been obtained.

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Acknowledgements

The authors would like to thank the Ministry of Communications and Information Technology, Government of India for providing the necessary fund to carry out the research work. The authors would like to thank Dr. Kaushik Chakraborty of Chittaranjan National Medical College and Hospital for his relentless support in providing the patient data. Thanks are also due to Dr. Chirasree Roy Chaudhuri of Bengal Engineering and Science University and Dr. Soumyabrata Roy Chaudhuri of Woodlands Hospital and Medical Research Centre for giving valuable suggestions regarding the validation of the system. The authors would also like to thank Prof. Amit Konar of the Department of Electronics and Telecommunication Engineering, Jadavpur University for his suggestions during the development of the system. Thanks are also due to Mr. Rajib Samadder for his support during the FPGA based implementation of the system.

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Authors and Affiliations

  1. IC Design and Fabrication Centre, Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, 700032, India

    Shubhajit Roy Chowdhury & Hiranmay Saha

  2. Om HealthNet Telemedicine Private Limited, Kolkata, 700055, India

    Dipankar Chakrabarti

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  1. Shubhajit Roy Chowdhury
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  2. Dipankar Chakrabarti
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Correspondence to Hiranmay Saha.

Appendix

Appendix

The pseudo-code of the APPSO4 algorithm is stated below.:

Algorithm APPSO4

Input: Randomly initialize position and velocity of particles in an n + 1 dimensional search space in case of an n dimensional problem;

Output: Position of the approximate global optimum;

Begin

Initialize the perception radius, the maximum and minimum number of observing directions, maximum and minimum number of sample points along any observing direction and the maximum velocity of a particle;

/*This is also the minimum number of observing directions and minimum number of sampling points along any direction*/

Set personal best position of a particle as the initial position of the particle;

While terminating condition not reached do

Begin

For I =1 to number of particles

Randomly choose the position of the neighboring particle;

Update the local best position;

Update the velocity of particle;

Evaluate the fitness function;

If the present performance is better than the performance at personal best position then

Update personal best position of the particle;

Minimize the spacing between the sample points along any direction within limits;

Increase the number of sampling directions within limits;

Else if the present performance is worse than the performance at personal best position then

Increase the spacing between the sample points along any direction within limits;

Minimize the number of sampling directions within limits;

Else

Keep the spacing between the sample points and the number of sampling directions unaltered;

End if;

End if;

Increment I;

End for;

End while;

End

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Roy Chowdhury, S., Chakrabarti, D. & Saha, H. Medical Diagnosis Using Adaptive Perceptive Particle Swarm Optimization and Its Hardware Realization using Field Programmable Gate Array. J Med Syst 33, 447–465 (2009). https://doi.org/10.1007/s10916-008-9206-0

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