CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Ensemble based Machine Learning Approach for Patient Health Monitoring

by Maruf Rahman, Joydeep Roy, Tanuja Das
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 24
Year of Publication: 2024
Authors: Maruf Rahman, Joydeep Roy, Tanuja Das
10.5120/ijca2024923698

Maruf Rahman, Joydeep Roy, Tanuja Das . Ensemble based Machine Learning Approach for Patient Health Monitoring. International Journal of Computer Applications. 186, 24 ( Jun 2024), 1-9. DOI=10.5120/ijca2024923698

@article{ 10.5120/ijca2024923698,
author = { Maruf Rahman, Joydeep Roy, Tanuja Das },
title = { Ensemble based Machine Learning Approach for Patient Health Monitoring },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2024 },
volume = { 186 },
number = { 24 },
month = { Jun },
year = { 2024 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number24/ensemble-based-machine-learning-approach-for-patient-health-monitoring/ },
doi = { 10.5120/ijca2024923698 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-06-27T00:56:26.952534+05:30
%A Maruf Rahman
%A Joydeep Roy
%A Tanuja Das
%T Ensemble based Machine Learning Approach for Patient Health Monitoring
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 24
%P 1-9
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the present era, healthcare analysis has been continuously evolving to serve the medical demands of the patients. Due to the advancement in the sensor technology, to be used in the wearable health devices for monitoring the health status of the people, enormous quantity of data is being produced progressively. Wearable healthcare equipment is essential for early detection and treatment of chronic diseases. An in-depth study is required to design a healthcare system that collects, records, analyses and share large data streams containing medical information of the users in real-time and efficiently to increase health risks and reduce health-related costs. Many machine learning based approaches have been prominent in the classification of sensors data from these healthcare devices. In this work, an ensemble based classifier has been designed based on majority voting consisting of five types of classifiers namely, logistic regression, XGB classifier, support vector machines, AdaBoost classifier and artificial neural networks from which the results of the physiological sensor data are predicted from the clinical environment. The models are evaluated using four evaluation metrics that are confusion matrix, accuracy, precision and recall.

References
  1. C Amutha et al. Iot based cloud agent system for adult health care monitoring. International Research Journal of Multidisciplinary Technovation, 2(5):35–41, 2020.
  2. Stephanie Baker, Wei Xiang, and Ian Atkinson. Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach. Scientific Reports, 10(1):1–12, 2020.
  3. Stefano A Bini, Romil F Shah, Ilya Bendich, Joseph T Patterson, Kevin M Hwang, and Musa B Zaid. Machine learning algorithms can use wearable sensor data to accurately predict six-week patient-reported outcome scores following joint replacement in a prospective trial. The Journal of Arthroplasty, 34(10):2242–2247, 2019.
  4. Massimo Buscema. A brief overview and introduction to artificial neural networks. Substance use & misuse, 37(8- 10):1093–1148, 2002.
  5. Colin Campbell. An introduction to kernel methods. Studies in Fuzziness and Soft Computing, 66:155–192, 2001.
  6. Pablo Carvalho, Esteban Clua, Aline Paes, Cristiana Bentes, Bruno Lopes, and L´ucia Maria de A Drummond. Using machine learning techniques to analyze the performance of concurrent kernel execution on gpus. Future Generation Computer Systems, 113:528–540, 2020.
  7. Zhuo Chen, Fu Jiang, Yijun Cheng, Xin Gu, Weirong Liu, and Jun Peng. Xgboost classifier for ddos attack detection and analysis in sdn-based cloud. In 2018 IEEE international conference on big data and smart computing (bigcomp), pages 251–256. IEEE, 2018.
  8. Evangelia Christodoulou, Jie Ma, Gary S Collins, Ewout W Steyerberg, Jan Y Verbakel, and Ben Van Calster. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. Journal of clinical epidemiology, 110:12–22, 2019.
  9. Bal´azs Csan´ad Cs´aji et al. Approximation with artificial neural networks. Faculty of Sciences, Etvs Lornd University, Hungary, 24(48):7, 2001.
  10. Cristiano Andr´e da Costa, Cristian F Pasluosta, Bjorn Eskofier, Denise Bandeira da Silva, and Rodrigo da Rosa Righi.Internet of health things: Toward intelligent vital signs monitoring in hospital wards. Artificial intelligence in medicine, 89:61–69, 2018.
  11. Adam D DeVore, Jedrek Wosik, and Adrian F Hernandez. The future of wearables in heart failure patients. JACC: Heart Failure, 7(11):922–932, 2019.
  12. Duarte Dias and Jo˜ao Paulo Silva Cunha.Wearable health devices— vital sign monitoring, systems and technologies. Sensors, 18(8):2414, 2018.
  13. Thomas G Dietterich et al. Ensemble learning. The handbook of brain theory and neural networks, 2:110–125, 2002.
  14. Yoav Freund and Robert E Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences, 55(1):119–139, 1997.
  15. Mikel Galar, Alberto Fern´andez, Edurne Barrenechea, Humberto Bustince, and Francisco Herrera. An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes. Pattern Recognition, 44(8):1761–1776, 2011.
  16. Trevor Hastie, Saharon Rosset, Ji Zhu, and Hui Zou. Multiclass adaboost. Statistics and its Interface, 2(3):349–360, 2009.
  17. Joseph M Hilbe. Logistic regression models. CRC press, 2009.
  18. Wei-Chun Hsu, Tommy Sugiarto, Yi-Jia Lin, Fu-Chi Yang, Zheng-Yi Lin, Chi-Tien Sun, Chun-Lung Hsu, and Kuan- Nien Chou. Multiple-wearable-sensor-based gait classification and analysis in patients with neurological disorders. Sensors, 18(10):3397, 2018.
  19. Miguel Enrique Iglesias Mart´ınez, Juan M Garc´ıa-Gomez, Carlos S´aez, Pedro Fern´andez de C´ordoba, and J Alberto Conejero. Feature extraction and similarity of movement detection during sleep, based on higher order spectra and entropy of the actigraphy signal: Results of the Hispanic Community Health Study/Study of Latinos. Sensors, 18(12):4310, 2018.
  20. Yoshifusa Ito. Representation of functions by superpositions of a step or sigmoid function and their applications to neural network theory. Neural Networks, 4(3):385–394, 1991.
  21. Suchitra Kataria and Vinod Ravindran. Emerging role of ehealth in the identification of very early inflammatory rheumatic diseases. Best Practice & Research Clinical Rheumatology, 33(4):101429, 2019.
  22. Pavleen Kaur, Ravinder Kumar, and Munish Kumar. A healthcare monitoring system using random forest and internet of things (iot). Multimedia Tools and Applications, 78(14):19905–19916, 2019.
  23. Sidney Le, Jana Hoffman, Christopher Barton, Julie C Fitzgerald, Angier Allen, Emily Pellegrini, Jacob Calvert, and Ritankar Das. Pediatric severe sepsis prediction using machine learning. Frontiers in pediatrics, 7:413, 2019.
  24. Charlene JY Liew, Pavitra Krishnaswamy, LT Cheng, Cher Heng Tan, Angeline CC Poh, and Tchoyoson CC Lim. Artificial intelligence and radiology in singapore: championing a new age of augmented imaging for unsurpassed patient care. Ann Acad Med Singapore, 48(1):16–24, 2019.
  25. David Liu, Matthias G¨orges, and Simon A Jenkins. University of queensland vital signs dataset: development of an accessible repository of anesthesia patient monitoring data for research. Anesthesia & Analgesia, 114(3):584–589, 2012.
  26. Carissa A Low. Harnessing consumer smartphone and wearable sensors for clinical cancer research. NPJ Digital Medicine, 3(1):1–7, 2020.
  27. Diego A Martinez, Scott R Levin, Eili Y Klein, Chirag R Parikh, Steven Menez, Richard A Taylor, and Jeremiah S Hinson. Early prediction of acute kidney injury in the emergency department with machine-learning methods applied to electronic health record data. Annals of emergency medicine, 76(4):501–514, 2020.
  28. Nimrabanu Memon, Samir B Patel, and Dhruvesh P Patel. Comparative analysis of artificial neural network and xgboost algorithm for polsar image classification. In International Conference on Pattern Recognition and Machine Intelligence, pages 452–460. Springer, 2019.
  29. Scott Menard. Applied logistic regression analysis, volume 106. Sage, 2002.
  30. Bhagyashree Mohanta, Priti Das, and Srikanta Patnaik. Healthcare 5.0: A paradigm shift in digital healthcare system using artificial intelligence, iot and 5g communication. In 2019 International Conference on Applied Machine Learning (ICAML), pages 191–196. IEEE, 2019.
  31. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of machine learning. MIT press, 2018.
  32. William S Noble. What is a support vector machine? Nature biotechnology, 24(12):1565–1567, 2006.
  33. Henry Friday Nweke, Ying Wah Teh, Ghulam Mujtaba, and Mohammed Ali Al-Garadi. Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions. Information Fusion, 46:147–170, 2019.
  34. Salome Oniani, Gonc¸alo Marques, Sophio Barnovi, Ivan Miguel Pires, and Akash Kumar Bhoi. Artificial intelligence for internet of things and enhanced medical systems. In Bio-inspired Neurocomputing, pages 43–59. Springer, 2021.
  35. JR Parker. Rank and response combination from confusion matrix data. Information fusion, 2(2):113–120, 2001.
  36. Robi Polikar. Ensemble learning. In Ensemble machine learning, pages 1–34. Springer, 2012.
  37. Giovanni Rubeis. The disruptive power of artificial intelligence. ethical aspects of gerontechnology in elderly care. Archives of Gerontology and Geriatrics, 91:104186, 2020.
  38. Barret Rush, Leo Anthony Celi, and David J Stone. Applying machine learning to continuously monitored physiological data. Journal of clinical monitoring and computing, 33(5):887–893, 2019.
  39. Sriparna Saha and Asif Ekbal. Combining multiple classifiers using vote based classifier ensemble technique for named entity recognition. Data & Knowledge Engineering, 85:15–39, 2013.
  40. Prasan Kumar Sahoo, Hiren Kumar Thakkar, and Ming-Yih Lee. A cardiac early warning system with multi channel scg and ecg monitoring for mobile health. Sensors, 17(4):711, 2017.
  41. Roni Shouval, Joshua A Fein, Bipin Savani, Mohamad Mohty, and Arnon Nagler. Machine learning and artificial intelligence in haematology. British Journal of Haematology, 192(2):239–250, 2021.
  42. Alireza Souri, Marwan Yassin Ghafour, Aram Mahmood Ahmed, Fatemeh Safara, Ali Yamini, and Mahdi Hoseyninezhad. A new machine learning-based healthcare monitoring model for student’s condition diagnosis in internet of things environment. Soft Computing, 24:17111–17121, 2020.
  43. Gregor Stiglic, Primoz Kocbek, Nino Fijacko, Marinka Zitnik, Katrien Verbert, and Leona Cilar. Interpretability of machine learning-based prediction models in healthcare. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(5):e1379, 2020.
  44. Xiaohui Tao, Thanveer Basha Shaik, Niall Higgins, Raj Gururajan, and Xujuan Zhou. Remote patient monitoring using radio frequency identification (rfid) technology and machine learning for early detection of suicidal behaviour in mental health facilities. Sensors, 21(3):776, 2021.
  45. Saurabh Singh Thakur, Shabbir Syed Abdul, Hsiao- Yean Shannon Chiu, Ram Babu Roy, Po-Yu Huang, Shwetambara Malwade, Aldilas Achmad Nursetyo, and Yu- Chuan Jack Li. Artificial-intelligence-based prediction of clinical events among hemodialysis patients using noncontact sensor data. Sensors, 18(9):2833, 2018.
  46. Maria Gabriela Valle Gottlieb, Vera Elizabeth Closs, Vilma Maria Junges, and Carla Helena Augustin Schwanke. Impact of human aging and modern lifestyle on gut microbiota. Critical reviews in food science and nutrition, 58(9):1557–1564, 2018.
  47. Rajat Vashistha, Arun Kumar Dangi, Ashwani Kumar, Deepak Chhabra, and Pratyoosh Shukla. Futuristic biosensors for cardiac health care: an artificial intelligence approach. 3 Biotech, 8(8):1–11, 2018.
  48. Simon T Vistisen, Alistair EW Johnson, and Thomas WL Scheeren. Predicting vital sign deterioration with artificial intelligence or machine learning, 2019.
  49. Lipo Wang. Support vector machines: theory and applications, volume 177. Springer Science & Business Media, 2005.
  50. Jia Wu, Liu Chang, and Genghua Yu. Effective data decisionmaking and transmission system based on mobile health for chronic disease management in the elderly. IEEE Systems Journal, 2020.
Index Terms

Computer Science
Information Sciences
Machine Learning
Health monitoring

Keywords

Vital signs sensor data logistic regression XGB classifier support vector machines AdaBoost classifier artificial neural networks ensemble learning

Powered by PhDFocusTM