Diagnosing of Disease using Machine Learning in Healthcare by Internet of Things

Authors

  • Vivek Veeraiah Research Scholar, Department of Computer Science, Adichunchanagiri University, Mandya, Karnataka, India
  • Ravikaumar G. K. Professor, Department of Computer Science and Engineering, Adichunchanagiri University, Mandya, Karnataka, India
  • Neeraj Gupta Department of Information Technology, Panipat Institute of Engineering and Technology, Panipat, Haryana, India
  • Dinesh Singh Department of Technical Education, Government Polytechnic, Sonipat, Haryana, India
  • Vinod Motiram Rathod Assistant Professor, Department of Computer Science and Engineering, Bharati Vidyapeeth Deemed University Department of Engineering and Technology, Navi Mumbai, Maharashtra, India
  • Rama Krishna Yellapragada Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram -522502, Guntur, Andhra Pradesh, India

Keywords:

IoT in healthcare, Machine Learning, Diagnose, Disease, Clinical interaction, Learning, Dataset

Abstract

AI (ML) is an integral asset that conveys experiences concealed in Internet of Things (IoT) information. These mixture advances work intelligently to further develop the dynamic cycle in various regions like training, defense, general works, and the medical services. ML enables the IoT to illustratestowed away examples in mass information to get ideal forecast and proposal frameworks. Clinical records created by the mechanized machines, foresee infection analyze and direct ongoing observation of affected people is within reach as IOT and ML is embraced by medical care.Each AI calculations perform contrastingly on various datasets. Because of prescient outcomes changing, this could affect the general outcomes. The variety in expectation results poses a potential threat in the clinical dynamic interaction. Thusly, one fundamental is to comprehend various ML calculations utilized to deal with Internet of things information inmedical services area. In this paper features notable ML calculations for grouping and expectation and shows how they have been utilized in the medical care area. The point of this paper is to introduce an extensive outline of existing ML draws near and their application in IoT clinical information. In an intensive examination, we see that different ML expectation calculations have different weaknesses. Contingent upon the sort of IoT dataset, we want to pick an ideal strategy to foresee basic medical care information. The paper additionally gives a few instances of IoT and AI to anticipate future medical care framework patterns.

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References

Wang, M.-H.; Chen, H.-K.; Hsu, M.-H.; Wang, H.-C.; Yeh, Y.-T. Cloud Computing for Infectious Disease Surveillance and Control:Development and Evaluation of a Hospital Automated Laboratory Reporting System. J. Med. Internet Res. 2018, 20, e10886.

Abed, M., &Ibrikci, T. (2019). Comparison between machine learning algorithms inthe predicting the onset of diabetes. In 2019 international artificial intelligence anddata processing symposium (IDAP) (pp. 1–5). IEEE, http://dx.doi.org/10.1109/IDAP.2019.8875965.

Ahmad, M., Tundjungsari, V., Widianti, D., Amalia, P., &Rachmawati, U. A. (2017).Diagnostic decision support system of chronic kidney disease using support vectormachine. In 2017 second international conference on informatics and computing (ICIC)(pp. 1–4). IEEE, http://dx.doi.org/10.1109/IAC.2017.8280576.19

Charleonnan, A., Fufaung, T., Niyomwong, T., Chokchueypattanakit, W., Suwan-nawach, S., &Ninchawee, N. (2016). Predictive analytics for chronic kidney diseaseusing machine learning techniques. In 2016 management and innovation technologyinternational conference (MITicon) (pp. MIT–80). IEEE, http://dx.doi.org/10.1109/MITICON.2016.8025242.

Chatterjee, S., Banerjee, S., Basu, P., Debnath, M., &Sen, S. (2017). Cuckoo searchcoupled artificial neural network in detection of chronic kidney disease. In2017 1st international conference on electronics, materials engineering and nano-technology (IEMENTech) (pp. 1–4). IEEE, http://dx.doi.org/10.1109/IEMENTECH.2017.8077016.

Chen, W., Chen, S., Zhang, H., & Wu, T. (2017). A hybrid prediction model fortype 2 diabetes using K-means and decision tree. In 2017 8th IEEE internationalconference on software engineering and service science (ICSESS) (pp. 386–390). IEEE,http://dx.doi.org/10.1109/ICSESS.2017.8342938.

Chen, Z., Zhang, Z., Zhu, R., Xiang, Y., & Harrington, P. B. (2016). Diagnosis of patientswith chronic kidney disease by using two fuzzy classifiers. Chemometrics andIntelligent Laboratory Systems, 153, 140–145. http://dx.doi.org/10.1016/j.chemolab.2016.03.004.

F. Jiang, Y. Jiang, H. Zhi et al., “Artificial intelligence inhealthcare: past, present and future,” Stroke and VascularNeurology, vol. 2, no. 4, pp. 230–243, 2017.

Sidey-Gibbons, A. M. Jenni, and C. J. Sidey-Gibbons, “Machine learning in medicine: a practical introduction,” BMCMedical Research Methodology, vol. 19, 2019.

T. Davenport and R. Kalakota, “the potential for artificialintelligence in healthcare,” Future Healthcare Journal, vol. 6,no. 2, pp. 94–98, 2019.

J. Keto, H. Ventola, J. Jokelainen et al., “cardiovascular disease risk factors in relation to smoking behaviour andhistory: a population-based cohort study,” Open Heart, vol. 3,no. 2, 2016.

A. B. Olokoba, O. A. Obateru, and L. B. Olokoba, “Type 2diabetes mellitus: a review of current trends,” Oman MedicalJournal, vol. 27, no. 4, pp. 269–273, 2012.

D. Singh and V. Kumar, “Single image defogging by gaingradient image filter,” Science China Information Sciences,vol. 62, no. 7, pp. 1–3, 2019.

I. Sartzetakis, K. Christodoulopoulos, and E. Varvarigos,“Accurate quality of transmission estimation with machinelearning,” Journal of Optical Communications and Networking, vol. 11, no. 3, pp. 140–150, 2019.

S. Otoum, B. Kantarci, and H. T. Mouftah, “On the feasibilityof deep learning in sensor network intrusion detection,” IEEENetworking Letters, vol. 1, no. 2, pp. 68–71, 2019.

Q. Zou, K. Qu, Y. Luo, D. Yin, Y. Ju, and H. Tang, “Predictingdiabetes mellitus with machine learning techniques,” Frontiers in Genetics, vol. 9, p. 515, 2018.

A. Misra, H. Gopalan, R. Jayawardena et al., “Diabetes indeveloping countries,” Journal of Diabetes, vol. 11, no. 7,pp. 522–539, 2019.

D. Leroith, G. J. Biessels, S. S. Braithwaite et al., “Treatment ofdiabetes in older adults: an endocrine society∗ clinicalpractice guideline,”

https://www.kaggle.com/sudalairajkumar/novel-coronavirus-2019 dataset#COVID19_open_line_list.csv.

https://www.kaggle.com/sulianova/cardiovascular-diseasedataset.

https://www.kaggle.com/uciml/pima-indians-diabetes-database.

J. Smith Walter, “Using the ADAP learning algorithm toforecast the onset of diabetes mellitus,” Johns Hopkins AplTechnical Digest, vol. 10, 1988.

N. S. Artzi, S. Shilo, E. Hadar et al., “Prediction of gestationaldiabetes based on nationwide electronic health records,”Nature Medicine, vol. 26, no. 1, pp. 71–76, 2020.

V. Raman, P. ,en, and P. Sumari, “Proposed retinal abnormality detection and classificationapproach: computeraided detection for diabetic retinopathy by machine learningapproaches,” in Proceedings of the 2016 8th IEEE InternationalConference on Communication Software and Networks, Beijing, China, June 2016.

J. Ramsingh and V. Bhuvaneswari, “An efficient map reducebased hybrid NBC-TFIDF algorithm to mine the publicsentiment on diabetes mellitus–a big data approach,” Journalof King Saud UniversityComputer and Information Sciences,2018, In press.

WHO, Global Health Risks Report 2016, https://www.who.int/diabetes/global-report, World Health Organization, Geneva,Switzerland, 2020, https://www.who.int/diabetes/globalreport.

N. Sharma and A. Singh, “Diabetes detection and prediction using machine learning/IoT: a survey,” Communications inComputer and Information Science, vol. 18, pp. 471–479, 2018.

S. Afzali and O. Yildiz, “An effective sample preparationmethod for diabetes prediction,” :e International ArabJournal of Information Technology, vol. 15, no. 6, 2018.

N. eera-Umpon, I. Poonkasem, S. Auephanwiriyakul, andD. Patikulsila, “Hard exudate detection in retinal fundusimages using supervised learning,” Neural Computing &Applications, vol. 32, 2020.

Q. Zou, K. Qu, Y. Luo, D. Yin, Y. Ju, and H. Tang, “Predictingdiabetes mellitus with machine learning techniques,” Frontiers in Genetics, vol. 9, 2018.

M. Alghamdi, M. Al-Mallah, S. Keteyian, C. Brawner,J. Ehrman, and S. Sakr, “Predicting diabetes mellitus usingSMOTE and ensemble machine learning approach: the HenryFord exercise testing (FIT) project,” PLoS One, vol. 12, 2017.

M. Hammad, M. H. Alkinani, B. B. Gupta, and A. A. A. ElLatif, “Myocardial infarction detection based on the deepneural network on imbalanced data,” Multimedia Systems,vol. 27, 2021.

Z. Ali, M. S. Hossain, G. Muhammad, and A. K. Sangaiah,“An intelligent healthcare system for detection and classification to discriminate vocal fold disorders,” Future Generation Computer Systems, vol. 85, pp. 19–28, 2018.

G. Yang, L. Xie, M. Mantysalo et al., “A health-IoT platformbased on the integration of intelligent packaging, unobtrusive bio-sensor, and intelligent medicine box,” IEEETransactions on Industrial Informatics, vol. 10, no. 4,pp. 2180–2191, 2014.

Y. Yan, “A home-based health information acquisitionsystem,” Health Information Science and Systems, vol. 1, p. 12,2013.

M. Khan, K. Han, and S. Karthik, “Designing smart control systems based on internet of things and big data analytics,”Wireless Personal Communications, vol. 99, no. 4,pp. 1683–1697, 2018.

P. J. Nachankar, “IOT in agriculture,” Decision Making,vol. 1, no. 3, 2018.

V. G. Menon, “An IoT-enabled intelligent automobile system for smart cities,” Internet of @ings, p. 100213, 2020.

E. Qin, “Cloud computing and the internet of things:technology innovation in automobile service,” in Proceedingsof the International Conference on Human Interface and theManagement of Information, pp. 173–180, Las Vegas, NV,USA, July 2013.

I. Froiz-M´ıguez, T. Fern´andez-Caram´es, P. Fraga-Lamas,and L. Castedo, “Design, implementation and practicalevaluation of an IoT home automation system for fogcomputing applications based on MQTT and ZigBee-WiFisensor nodes,” Sensors, vol. 18, no. 8, p. 2660, 2018.

P. S. Mathew, “Applications of IoT in healthcare,” in Cognitive Computing for Big Data Systems over IoT, pp. 263–288,Springer, Berlin, Germany, 2018.

V. Jagadeeswari, “A study on medical Internet of +ings andBig Data in personalized healthcare system,” Health Information Science And Systems, vol. 6, p. 14, 2018.

Zoabi, Y.; Deri-Rozov, S.; Shomron, N. Machine learning-based prediction of COVID-19 diagnosis based on symptoms. NPJ Digit. Med. 2021, 4, 1–5.

Wilkinson, J.; Arnold, K.F.; Murray, E.J.; van Smeden, M.; Carr, K.; Sippy, R.; de Kamps, M.; Beam, A.; Konigorski, S.; Lippert, C.; et al. Time to reality check the promises of machine learning-powered precision medicine. Lancet Digit. Health 2020, 2, e677–e680.

Ahmed, Z.; Mohamed, K.; Zeeshan, S.; Dong, X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database J. Biol. Databases Curation 2020, 2020.

Veeraiah V., Ahamad G. P, S., Talukdar S. B., Gupta A. and Talukdar V., (2022) Enhancement of Meta Verse Capabilities by IoT Integration. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 1493-1498.doi: 10.1109/ICACITE53722.2022.9823766.

P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya and A. Gupta, "Detection of Liver Disease Using Machine Learning Approach," 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), Uttar Pradesh, India, 2022, pp. 1824-1829, doi: 10.1109/IC3I56241.2022.10073425.

Babu, S.Z.D. et al. (2022). Analysation of Big Data in Smart Healthcare. In: Gupta, M., Ghatak, S., Gupta, A., Mukherjee, A.L. (eds) Artificial Intelligence on Medical Data. Lecture Notes in Computational Vision and Biomechanics, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-19-0151-5_21

Bansal R., Gupta A., Singh R. and Nassa V. K., (2021). Role and Impact of Digital Technologies in E-Learning amidst COVID-19 Pandemic. 2021 Fourth International Conference on Computational Intelligence and Communication Technologies (CCICT), pp. 194-202.doi: 10.1109/CCICT53244.2021.00046.

Dushyant, K., Muskan, G., Gupta, A. and Pramanik, S. (2022). Utilizing Machine Learning and Deep Learning in Cyber security: An Innovative Approach”, in Cyber security and Digital Forensics, M. M. Ghonge, S. Pramanik, R. Mangrulkar,D. N. Le, Eds, Wiley, https://doi.org/10.1002/9781119795667.ch12

Gupta A., Kaushik D., Garg M. and Verma A., (2020).Machine Learning model for Breast Cancer Prediction. 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 472-477.doi: 10.1109/I-SMAC49090.2020.9243323.

Kaushik, K., Garg, M., Annu, Gupta, A. and Pramanik, S. (2021). Application of Machine Learning and Deep Learning in Cyber security: An Innovative Approach, in Cybersecurity and Digital Forensics: Challenges and Future Trends, M. Ghonge, S. Pramanik, R. Mangrulkar and D. N. Le, Eds, Wiley, 2021.

Veeraiah, V., Khan, H., Kumar A., Ahamad S., Mahajan A. and Gupta A., (2022). Integration of PSO and Deep Learning for Trend Analysis of Meta-Verse. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 713-718.doi: 10.1109/ICACITE53722.2022.9823883.

V. Jain, S. M. Beram, V. Talukdar, T. Patil, D. Dhabliya and A. Gupta, "Accuracy Enhancement in Machine Learning During Blockchain Based Transaction Classification," 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC), Solan, Himachal Pradesh, India, 2022, pp. 536-540, doi: 10.1109/PDGC56933.2022.10053213.

V. Talukdar, D. Dhabliya, B. Kumar, S. B. Talukdar, S. Ahamad and A. Gupta, "Suspicious Activity Detection and Classification in IoT Environment Using Machine Learning Approach," 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC), Solan, Himachal Pradesh, India, 2022, pp. 531-535, doi: 10.1109/PDGC56933.2022.10053312.

P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya and A. Gupta, "A Scalable Platform to Collect, Store, Visualize and Analyze Big Data in Real- Time," 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM), Uttar Pradesh, India, 2023, pp. 1-6, doi: 10.1109/ICIPTM57143.2023.10118183.

M. Dhingra, D. Dhabliya, M. K. Dubey, A. Gupta and D. H. Reddy, "A Review on Comparison of Machine Learning Algorithms for Text Classification," 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), Uttar Pradesh, India, 2022, pp. 1818-1823, doi: 10.1109/IC3I56241.2022.10072502.

D. Mandal, A. Shukla, A. Ghosh, A. Gupta and D. Dhabliya, "Molecular Dynamics Simulation for Serial and Parallel Computation Using Leaf Frog Algorithm," 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC), Solan, Himachal Pradesh, India, 2022, pp. 552-557, doi: 10.1109/PDGC56933.2022.10053161.

P. R. Kshirsagar, D. H. Reddy, M. Dhingra, D. Dhabliya and A. Gupta, "A Review on Application of Deep Learning in Natural Language Processing," 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), Uttar Pradesh, India, 2022, pp. 1834-1840, doi: 10.1109/IC3I56241.2022.10073309.

V. V. Chellam, S. Praveenkumar, S. B. Talukdar, V. Talukdar, S. K. Jain and A. Gupta, "Development of a Blockchain-based Platform to Simplify the Sharing of Patient Data," 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM), Uttar Pradesh, India, 2023, pp. 1-6, doi: 10.1109/ICIPTM57143.2023.10118194.

Lalitha Kumari, P. et al. (2023). Methodology for Classifying Objects in High-Resolution Optical Images Using Deep Learning Techniques. In: Chakravarthy, V., Bhateja, V., Flores Fuentes, W., Anguera, J., Vasavi, K.P. (eds) Advances in Signal Processing, Embedded Systems and IoT . Lecture Notes in Electrical Engineering, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-19-8865-3_55

Sindhwani, N. et al. (2023). Comparative Analysis of Optimization Algorithms for Antenna Selection in MIMO Systems. In: Chakravarthy, V., Bhateja, V., Flores Fuentes, W., Anguera, J., Vasavi, K.P. (eds) Advances in Signal Processing, Embedded Systems and IoT . Lecture Notes in Electrical Engineering, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-19-8865-3_54

Veeraiah V., Rajaboina N. B., Rao G. N., Ahamad S., Gupta A. and Suri C. S., (2022).Securing Online Web Application for IoT Management. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 1499-1504.doi: 10.1109/ICACITE53722.2022.9823733.

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Published

16.08.2023

How to Cite

Veeraiah, V. ., G. K. , R. ., Gupta, N. ., Singh, D. ., Rathod, V. M. ., & Yellapragada, R. K. . (2023). Diagnosing of Disease using Machine Learning in Healthcare by Internet of Things. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 954–973. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3388

Issue

Vol. 11 No. 10s (2023)

Section

Research Article

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