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Artificial Intelligence and Internet of Things Based Healthcare 4.0 Monitoring System

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Abstract

Artificial Intelligence (AI) is widely implemented in healthcare 4.0 for producing early and accurate results. The early predictions of disease help doctors to make early decisions to save the life of patients. Internet of things (IoT) is working as a catalyst to enhance the power of AI applications in healthcare. The patients' data are captured by IoT_sensor and analysis of the patient data is performed by machine learning techniques. The main aim of the work is to propose a Machine learning-based healthcare model to early and accurately predict the different diseases. In this work, seven machine learning classification algorithms such as decision tree, support vector machine, Naïve Bayes, adaptive boosting, Random Forest (RF), artificial neural network, and K-nearest neighbor are used to predict the nine fatal diseases such as heart disease, diabetics breast cancer, hepatitis, liver disorder, dermatology, surgery data, thyroid, and spect heart. To evaluate the performance of the proposed model, four performance metrics (such as accuracy, sensitivity, specificity, and area under the curve) are used. The RF classifier observes the maximum accuracy of 97.62%, the sensitivity of 99.67%, specificity of 97.81%, and AUC of 99.32% for different diseases. The developed healthcare model will help doctors to diagnose the disease early.

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References

  1. Market research report. Retrieved March2021 from https://www.grandviewresearch.com/industry-analysis/machine-learning-market.

  2. Market research report. Retrieved March2021 from https://www.alliedmarketresearch.com/predictive-analytics-in-healthcare-market#:~:text=Predictive%20Analytics%20in%20Healthcare%20Market%20Overiew%3A&text=The%20global%20predictive%20analytics%20in,21.2%25%20from%202018%20to%202025.

  3. Market research report. Retrieved March2021 from https://www.grandviewresearch.com/press-release/global-artificial-intelligence-healthcare-market

  4. Market research report. Retrieved March2021 from https://www.grandviewresearch.com/industry-analysis/internet-of-things-iot-healthcare-market

  5. Market research report. Retrieved March2021 from https://www.appventurez.com/blog/iot-healthcare-future-scope/

  6. Kumari, A., Tanwar, S., Tyagi, S., & Kumar, N. (2018). Fog computing for Healthcare 4.0 environment: Opportunities and challenges. Computers and Electrical Engineering, 72, 1–13.

    Article  Google Scholar 

  7. Wu, J., Ping, L., Ge, X., Wang, Y., & Fu, J. (2010). Cloud storage as the infrastructure of cloud computing. In 2010 International conference on intelligent computing and cognitive informatics (pp. 380–383). IEEE.

  8. Perveen, S., Shahbaz, M., Guergachi, A., & Keshavjee, K. (2016). Performance analysis of data mining classification techniques to predict diabetes. Procedia Computer Science, 82, 115–121.

    Article  Google Scholar 

  9. Wu, C. C., Yeh, W. C., Hsu, W. D., Islam, M. M., Nguyen, P. A. A., Poly, T. N., Wang, Y. C., Yang, H. C., & Li, Y. C. J. (2019). Prediction of fatty liver disease using machine learning algorithms. Computer Methods and Programs in Biomedicine, 170, 23–29.

    Article  Google Scholar 

  10. Shankar, K., Lakshmanaprabu, S. K., Gupta, D., Maseleno, A., & De Albuquerque, V. H. C. (2020). Optimal feature-based multi-kernel SVM approach for thyroid disease classification. The Journal of Supercomputing, 76(2), 1128–1143.

    Article  Google Scholar 

  11. Sisodia, D., & Sisodia, D. S. (2018). Prediction of diabetes using classification algorithms. Procedia Computer Science, 132, 1578–1585.

    Article  Google Scholar 

  12. Kumar, N. K., & Vigneswari, D. (2019). Hepatitis-infectious disease prediction using classification algorithms. Research Journal of Pharmacy and Technology, 12(8), 3720–3725.

    Article  Google Scholar 

  13. Parisi, L., RaviChandran, N., & Manaog, M. L. (2020). A novel hybrid algorithm for aiding prediction of prognosis in patients with hepatitis. Neural Computing and Applications, 32(8), 3839–3852.

    Article  Google Scholar 

  14. Hameed, R. T., Mohamad, O. A., Hamid, O. T., & Tapus, N. (2015). Design of e-Healthcare management system based on cloud and service oriented architecture. In 2015 E-Health and bioengineering conference (EHB) (pp. 1–4). IEEE.

  15. Vijayarani, S., & Dhayanand, S. (2015). Data mining classification algorithms for kidney disease prediction. International Journal of Cybernetics Informatics, 4(4), 13–25.

    Article  Google Scholar 

  16. 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.

    Article  Google Scholar 

  17. Jahangir, M., Afzal, H., Ahmed, M., Khurshid, K., & Nawaz, R. (2017). An expert system for diabetes prediction using auto tuned multi-layer perceptron. In 2017 Intelligent systems conference (IntelliSys) (pp. 722–728). IEEE.

  18. Verma, L., Srivastava, S., & Negi, P. C. (2016). A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. Journal of Medical Systems, 40(7), 1–7.

    Article  Google Scholar 

  19. Haq, A. U., Li, J. P., Memon, M. H., Nazir, S., & Sun, R. (2018). A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms. Mobile Information Systems, 2018.

  20. Muhammad, Y., Tahir, M., Hayat, M., & Chong, K. T. (2020). Early and accurate detection and diagnosis of heart disease using intelligent computational model. Scientific Reports, 10(1), 1–17.

    Article  Google Scholar 

  21. Alkeshuosh, A. H., Moghadam, M. Z., Al Mansoori, I., & Abdar, M. (2017). Using PSO algorithm for producing best rules in diagnosis of heart disease. In 2017 International conference on computer and applications (ICCA) (pp. 306–311). IEEE.

  22. Samuel, O. W., Asogbon, G. M., Sangaiah, A. K., Fang, P., & Li, G. (2017). An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Systems with Applications, 68, 163–172.

    Article  Google Scholar 

  23. Ul Haq, A., Li, J., Ali, Z., Memon, M. H., Abbas, M., & Nazir, S. (2020). Recognition of the Parkinson’s disease using a hybrid feature selection approach. Journal of Intelligent & Fuzzy Systems, (Preprint), 39(1), 1319–1339, https://doi.org/10.3233/JIFS-200075.

  24. Mathur, P., Srivastava, S., Xu, X., & Mehta, J. L. (2020). Artificial intelligence, machine learning, and cardiovascular disease. Clinical Medicine Insights: Cardiology, 14, 1179546820927404.

    Google Scholar 

  25. Khourdifi, Y., & Bahaj, M. (2019). Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization. International Journal of Intelligent Engineering & Systems, 12(1), 242–252.

    Article  Google Scholar 

  26. Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y., & Tang, H. (2018). Predicting diabetes mellitus with machine learning techniques. Frontiers in Genetics, 9, 515.

    Article  Google Scholar 

  27. Joloudari, J. H., Saadatfar, H., Dehzangi, A., & Shamshirband, S. (2019). Computer-aided decision-making for predicting liver disease using PSO-based optimized SVM with feature selection. Informatics in Medicine Unlocked, 17, 100255.

    Article  Google Scholar 

  28. Song, Y. Y., & Ying, L. U. (2015). Decision tree methods: Applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130.

    Google Scholar 

  29. Shen, L., Chen, H., Yu, Z., Kang, W., Zhang, B., Li, H., Yang, B., & Liu, D. (2016). Evolving support vector machines using fruit fly optimization for medical data classification. Knowledge-Based Systems, 96, 61–75.

    Article  Google Scholar 

  30. Baitharu, T. R., & Pani, S. K. (2016). Analysis of data mining techniques for healthcare decision support system using liver disorder dataset. Procedia Computer Science, 85, 862–870.

    Article  Google Scholar 

  31. Alam, M. Z., Rahman, M. S., & Rahman, M. S. (2019). A Random Forest based predictor for medical data classification using feature ranking. Informatics in Medicine Unlocked, 15, 100180.

    Article  Google Scholar 

  32. Kishor, A., Chakraborty, C. H., & Jeberson, W. (2020). A novel fog computing approach for minimization of latency in healthcare using machine learning. International Journal of Interactive Multimedia and Artificial Intelligence. https://doi.org/10.9781/ijimai.2020.12.004

    Article  Google Scholar 

  33. Deng, Z., Zhu, X., Cheng, D., Zong, M., & Zhang, S. (2016). Efficient kNN classification algorithm for big data. Neurocomputing, 195, 143–148.

    Article  Google Scholar 

  34. Kishor, A., Chakraborty, C., & Jeberson, W. (2021). Reinforcement learning for medical information processing over heterogeneous networks. Multimed Tools Appl. https://doi.org/10.1007/s11042-021-10840-0

    Article  Google Scholar 

  35. Dwivedi, R., Dey, S., Chakraborty, C., & Tiwari, S. (2021). Grape disease detection network based on multi-task learning and attention features. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2021.3064060

    Article  Google Scholar 

  36. Chinmay, C. (2017). Chronic wound image analysis by particle swarm optimization technique for tele-wound network. International Journal of Wireless Personal Communications, 96(3), 3655–3671. https://doi.org/10.1007/s11277-017-4281-5

    Article  Google Scholar 

  37. Arindam, S., Mohammad, Z. A., Moirangthem, M. S., Abdulfattah, C. C., & Subhendu, K. P. (2021). Artificial neural synchronization using nature inspired whale optimization. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3052884

    Article  Google Scholar 

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Acknowledgements

The authors would like to thanks to Department of Computer Science and Engineering, Subharti Institute of Engineering and Technology, Swami Vivekanand Subharti University, Meerut, India to give this platform to work.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Swami Vivekanand Subharti University, Meerut, Uttar Pradesh, India

    Amit Kishor

  2. Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Jharkhand, India

    Chinmay Chakraborty

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  1. Amit Kishor
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Correspondence to Chinmay Chakraborty.

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Kishor, A., Chakraborty, C. Artificial Intelligence and Internet of Things Based Healthcare 4.0 Monitoring System. Wireless Pers Commun 127, 1615–1631 (2022). https://doi.org/10.1007/s11277-021-08708-5

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