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Journal of Applied Mathematics and Computation

DOI：http://dx.doi.org/10.26855/jamc.2023.03.002

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AI-based Smart Healthcare Disease Diagnosis: Analysis & Design of New Model

Raja Marappan*, S. Bhaskaran

School of Computing, SASTRA Deemed University, Thanjavur, India.

*Corresponding author: Raja Marappan

Date: February 14,2023　Hits: 1267

Abstract

Recently the provision of healthcare is considered one of the basic societal obligations. Nowadays, the healthcare systems are expanding at a rapid pace. For predicting the risk of diseases, different machine learning algorithms are used in several studies. Most of these algorithms are focused on a single disease, for example, to predict diabetes or cancer disease. Also due to digitization, a lot of data are being produced in the healthcare sector. This data can be studied, analyzed, and used for predictions in using Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL) strategies. To facilitate a common system to diagnose multiple diseases, a smart healthcare diagnosis system has been proposed in this research. The users can choose a disease prediction and give input and see if the person is suffering from that specific disease or not. The proposed methodology is also designed to predict multiple diseases using various intelligence-based learning strategies.

References

[1] Yaganteeswarudu, A. Multi Disease Prediction Model by using Machine Learning and Flask API. In 2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 1242-1246). June 2020.

[2] Harimoorthy, K., Thangavelu, M. (2021). Multi-disease prediction model using improved SVM-radial bias technique in healthcare monitoring system. Journal of Ambient Intelligence and Humanized Computing, 12(3), 3715-3723.

[3] Gárate-Escamila, A. K., El Hassani, A. H., Andrès, E. (2020). Classification models for heart disease prediction using feature selection and PCA. Informatics in Medicine Unlocked, 19, 100330.

[4] Salekin, A., Stankovic, J. (2016, October). Detection of chronic kidney disease and selecting important predictive attributes. In 2016 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 262-270). IEEE.

[5] Pasadana, I. A., Hartama, D., Zarlis, M., Sianipar, A. S., Munandar, A., Baeha, S., Alam, A. R. M. (2019, August). Chronic kidney disease prediction by using different decision tree techniques. In Journal of Physics: Conference Series (Vol. 1255, No. 1, p. 012024). IOP Publishing.

[6] Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., & Soufi, G. J. (2020). Deep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning. Medical image analysis, 65, 101794.

[7] Arias-Garzón, D., Alzate-Grisales, J. A., Orozco-Arias, S., Arteaga-Arteaga, H. B., Bravo-Ortiz, M. A., Mora-Rubio, A. Tabares -Soto, R. (2021). COVID-19 detection in X-ray images using convolutional neural networks. Machine Learning with Applications, 6, 100138.

[8] Bashir, S., Khan, Z. S., Khan, F. H., Anjum, A., & Bashir, K. (2019, January). Improving heart disease prediction using feature selection approaches. In 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST) (pp. 619-623). IEEE.

[9] Jain, R., Gupta, M., Taneja, S., Hemanth, D. J. (2021). Deep learning based detection and analysis of COVID-19 on chest X-ray images. Applied Intelligence, 51(3), 1690-1700.

[10] Zoabi, Y., Deri-Rozov, S., Shomron, N. (2021). Machine learning-based prediction of COVID-19 diagnosis based on symptoms. npj Digital Medicine, 4(1), 1-5.

[11] Bhaskaran, S.; Marappan, R.; Santhi, B. Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications. Mathematics 2021, 9, 197. https://doi.org/10.3390/math9020197. 

[12] Marappan, R., Sethumadhavan, G. Solving Graph Coloring Problem Using Divide and Conquer-Based Turbulent Particle Swarm Optimization. Arab J Sci Eng (2021). https://doi.org/10.1007/s13369-021-06323-x.

[13] Bhaskaran, S.; Marappan, R.; Santhi, B. Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets. Mathematics, 2020, 8, 1106. https://doi.org/10.3390/math8071106.

[14] Marappan, R.; Sethumadhavan, G. Complexity Analysis and Stochastic Convergence of Some Well-known Evolutionary Operators for Solving Graph Coloring Problem. Mathematics 2020, 8, 303. https://doi.org/10.3390/math8030303. 

[15] N. S. Anand, R. Marappan and G. Sethumadhavan. "Performance Analysis of SAR Image Speckle Filters and its Recent Challenges," 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2018, pp. 1-4, doi: 10.1109/ICCIC.2018.8782425.

[16] Marappan, R., Sethumadhavan, G. Solution to Graph Coloring Using Genetic and Tabu Search Procedures. Arab J Sci Eng 43, 525-542 (2018). https://doi.org/10.1007/s13369-017-2686-9.

[17] R. Marappan and G. Sethumadhavan, "Solving channel allocation problem using new genetic algorithm with clique partitioning method," 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2016, pp. 1-4, doi: 10.1109/ICCIC.2016.7919671.

[18] R. Marappan and G. Sethumadhavan. "Solution to graph coloring problem using divide and conquer based genetic method," 2016 International Conference on Information Communication and Embedded Systems (ICICES), 2016, pp. 1-5. 

doi: 10.1109/ICICES.2016.7518911.

[19] R. Marappan and G. Sethumadhavan. "Divide and conquer based genetic method for solving channel allocation," 2016 International Conference on Information Communication and Embedded Systems (ICICES), 2016, pp. 1-5. 

doi: 10.1109/ICICES.2016.7518914.

[20] Solving Fixed Channel Allocation using Hybrid Evolutionary Method. Raja Marappan, Gopalakrishnan Sethumadhavan MAT- EC Web of Conferences, 57, 02015 (2016). DOI: 10.1051/matecconf/20165702015.

[21] Marappan, R., & Sethumadhavan, G. (2015). Solving graph coloring problem for large graphs. Global Journal of Pure and Applied Mathematics, 11(4), 2487-2494.

[22] Marappan, R., & Sethumadhavan, G. (2015). Solution to Graph Coloring Problem using Evolutionary Optimization through Symmetry-Breaking Approach. International Journal of Applied Engineering Research, 10(10), 26573-26580.

[23] Marappan, R., & Sethumadhavan, G. (2015). Solution to graph coloring problem using heuristics and recursive backtracking. International Journal of Applied Engineering Research, 10(10), 25939-25944.

[24] G. Sethumadhavan and R. Marappan. "A genetic algorithm for graph coloring using single parent conflict gene crossover and mutation with conflict gene removal procedure," 2013 IEEE International Conference on Computational Intelligence and Computing Research, 2013, pp. 1-6. doi: 10.1109/ICCIC.2013.6724190.

[25] R. Marappan and G. Sethumadhavan. "A New Genetic Algorithm for Graph Coloring," 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation, 2013, pp. 49-54. doi: 10.1109/CIMSim.2013.17.

[26] Raja Marappan, Gopalakrishnan Sethumadhavan, R.K. Srihari. New approximation algorithms for solving graph coloring problem—An experimental approach, Perspectives in Science, Volume 8, 2016, Pages 384-387, ISSN 2213-0209.

https://doi.org/10.1016/j.pisc.2016.04.083.

[27] Raja Marappan, Gopalakrishnan Sethumadhavan, U. Harimoorthy. Solving channel allocation problem using new genetic operators—An experimental approach, Perspectives in Science, Volume 8, 2016, Pages 409-411, ISSN 2213-0209. 

https://doi.org/10.1016/j.pisc.2016.04.091.

[28] S. Balakrishnan, Tamilarasi Suresh, Raja Marappan. Analysis of Recent Trends in Solving NP Problems with New Research Directions Using Evolutionary Methods. International Journal of Research Publication and Reviews, Vol (2), Issue (8), (2021) Page 1429-1435.

[29] S. Bhaskaran; Raja Marappan. New Personalized Recommendation System for E-Learning. AshEse Journal of Physical Science. Vol. 5(5), pp. 063-067, August, 2021 ISSN: 2059-7827. 

DOI: http://www.ashese.co.uk/ajps-v5-issue-5/new-personalized-recommendation-system-for-e-learning.

[30] S. Balakrishnan, Tamilarasi Suresh, Raja Marappan. (2021). A New Multi-Objective Evolutionary Approach to Graph Coloring and Channel Allocation Problems. Journal of Applied Mathematics and Computation, 5(4), 252-263. 

DOI: http://dx.doi.org/10.26855/jamc.2021.12.003.

[31] Raja Marappan. A New Multi-Objective Optimization in Solving Graph Coloring and Wireless Networks Channels Allocation Problems. Int. J. Advanced Networking and Applications, Volume: 13, Issue: 02, Pages: 4891-4895 (2021).

[32] Raja Marappan, S. Bhaskaran, N. Aakaash, S. Mathu Mitha. (2022). Analysis of COVID-19 Prediction Models: Design & Analysis of New Machine Learning Approach. Journal of Applied Mathematics and Computation, 6(1), 121-126.  DOI: http://dx.doi.org/10.26855/jamc.2022.03.013.

[33] Raja Marappan, S. Bhaskaran, S. Ashwadh, H. Aathi Raj. (2022). Extraction of Drug Review Polarity Using Sentimental Analysis. Journal of Applied Mathematics and Computation, 6(2), 167-177. DOI: http://dx.doi.org/10.26855/jamc.2022.06.001.

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AI-based Smart Healthcare Disease Diagnosis: Analysis & Design of New Model

How to cite this paper:  Raja Marappan, S. Bhaskaran. (2023) AI-based Smart Healthcare Disease Diagnosis: Analysis & Design of New Model. Journal of Applied Mathematics and Computation, 7(1), 15-18.

DOI: http://dx.doi.org/10.26855/jamc.2023.03.002