Login Register Cart 0
Generic placeholder image

Recent Patents on Engineering

Editor-in-Chief

ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Intelligent Decision Support System in Healthcare using Machine Learning Models

Author(s): Anup Patnaik* and Krishna Prasad K.

Volume 18, Issue 5, 2024

Published on: 07 August, 2023

Article ID: e060623217715 Pages: 14

DOI: 10.2174/1872212118666230606145738

Price: $65

conference banner
Abstract

Background: The use of intelligent decision support systems (IDSS) is widespread in the healthcare industry, particularly for real-time data, client and family history datasets, and prevalent patient features.

Objective: A massive chunk of various kinds of health data sets, including sensor information, medical evidence, and omic statistics, are produced by the modern techniques in this field and eventually transferred to a machine learning (ML) element for extracting data, categorization, as well as mining.

Method: In recent times, many patents have been focused on healthcare monitoring; however, they do not adequately incorporate appropriate algorithms for data collection, analysis, and prediction. The data collected is used for predictive modelling, then additionally, machine learning techniques are assisting to compare acquired datasets mathematically for decision-making platforms that may learn to recognise the recent trend and anticipated future problems. Depending on the dataset type, ML-based techniques can assess the circumstances. Training datasets are crucial for correctly anticipating both current and emerging events as well as new challenges.

Results: Since the importance of data acquisition determines how well learning models function, any deformed data of the types of dirty data, noisy data, unstructured data, and inadequate information results in inaccurate detection, estimate, and prediction.

Conclusion: Additionally, in contrast to other approaches, the experimental findings demonstrate the usefulness of the proposed method as a widespread implementation of machine learning algorithms within healthcare systems.

Graphical Abstract

[1]
A. Suragala, and V. PapaRaoA, Demystifying disease identification and diagnosis using machine learning classification algorithms.Researchgate, IGI global publications, 2020.
[2]
M. Saleem, and J. Sidiq, "Diagnosis and Classification of Thyroid Disorder using Machine Learning-", Syst. Rev., 2020.
[3]
H.K. Bhuyan, T. Arun Sai, M. Charan, K. Vignesh Chowdary, and B. Brahma, "Analysis of classification based predicted disease using machine learning and medical things model", 2022 Second international conference on advances in electrical, computing, communication and sustainable technologies (ICAECT),.
April 21-22, 2022, Bhilai, India, pp. 1-6, 2022. [http://dx.doi.org/10.1109/ICAECT54875.2022.9807903]
[4]
K. Adapa, M. Pillai, S. Das, P. Mosaly, and L. Mazur, "Predicting objective performance using perceived cognitive workload data in healthcare professionals: A machine learning study", Stud. Health Technol. Inform., vol. 290, pp. 809-813, 2022.
[http://dx.doi.org/10.3233/SHTI220191] [PMID: 35673130]
[5]
S. Verma, R. Popli, H. Kumar, and A. Srivastava, "Classification of thyroid diseases using machine learning frameworks", Int. J. Health Sci., pp. 7552-7566, 2022.
[http://dx.doi.org/10.53730/ijhs.v6nS1.6603]
[6]
J. Seo, T.H. Laine, G. Oh, and K.A. Sohn, "EEG-based emotion classification for alzheimer’s disease patients using conventional machine learning and recurrent neural network models", Sensors, vol. 20, no. 24, p. 7212, 2020.
[http://dx.doi.org/10.3390/s20247212] [PMID: 33339334]
[7]
L. Eloutouate, F. Elouaai, H.G. Tani, and M. Bouhorma, "Home automation and machine learning models for health monitoring", BDIoT 2021, pp. 362-372, .
[8]
T.H. Rafi, R.M. Shubair, F. Farhan, M.Z. Hoque, and F.M. Quayyum, "Recent advances in computer-aided medical diagnosis using machine learning algorithms with optimization techniques", IEEE Access, vol. 9, pp. 137847-137868, 2021.
[http://dx.doi.org/10.1109/ACCESS.2021.3108892]
[9]
B. Koval, I. Khlevna, and S. Shabatska, Development of heart disease diagnosis concept using machine learning., IT&I Workshops, 2021.
[10]
E. Shakeri, E.A. Mohammed, H.A.Z. Shakeri, and B.H. Far, "Exploring features contributing to the early prediction of sepsis using machine learning." 43rd Annual international conference of the IEEE engineering in medicine & biology society (EMBC) . Nov 01-05, 2021, Mexico, pp. 2472-2475, 2021.
[11]
A.Y. Darveshwala, D. Singh, and Y. Farooqui, "Chronic kidney disease stage identification in hiv infected patients using machine learning", 5th International conference on computing methodologies and communication (ICCMC). April 08-10, 2021, Erode, India, pp. 1509-1514, 2021.
[12]
A.U. Mazlan, N.A. Sahabudin, M.A. Remli, N.S. Ismail, M.S. Mohamad, and N.B. Warif, "Supervised and unsupervised machine learning for cancer classification: Recent development", IEEE international conference on automatic control & intelligent systems (I2CACIS). June 26-26, 2021, Shah Alam, Malaysia, pp. 392-395, 2021.
[13]
G. Shilimkar, A. Bhilare, and S. Pisal, "Disease prediction using machine learning", Int. J. Sci. Res. Sci. Technol., pp. 551-555, 2021.
[http://dx.doi.org/10.32628/IJSRST12183118]
[14]
M.F. Alrifaie, Z. Hussain, A. Shakir, and M. Lafta, "Using machine learning technologies to classify and predict heart disease", Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 3, p. 12, 2021.
[http://dx.doi.org/10.14569/IJACSA.2021.0120315]
[15]
D.M. Rao, D.R. Mohana, V. Talasila, and M. SureshKumar, "Prediction of chronic diseases at an early phase using machine learning approach", Research gate, 2021.
[16]
G. Luo, B.L. Stone, and M.D. Johnson, " et al.,Automating construction of machine learning models with clinical big data: Proposal rationale and methods", JMIR Res. Protoc., vol. 6, no. 8, p. e175, 2017.
[17]
G. Feretzakis, A. Sakagianni, and D. Kalles, " et al., Using machine learning for predicting the hospitalization of emergency department patients", Stud. Health Technol. Inform., vol. 295, pp. 405-408, 2022.
[http://dx.doi.org/10.3233/SHTI220751] [PMID: 35773897]
[18]
S. Samet, M.R. Laouar, I. Bendib, and S. Eom, "Analysis and prediction of diabetes disease using machine learning methods", Int. J. Decis. Support Syst. Technol., vol. 14, no. 1, pp. 1-19, 2022.
[http://dx.doi.org/10.4018/IJDSST.303943]
[19]
A.R. Shenoy, and B.R K., "Performance analysis of class imbalance handling techniques for early sepsis prediction using machine learning algorithms", 8th International Conference on Smart Structures and Systems (ICSSS), April 21-22, 2022, Chennai, India, pp. 1-6, 2022",
[20]
D.C. Bhavya, and S.P. Prakash, "A comparative analysis of machine learning algorithms for healthcare device data of social IoT", proceeding of the 25th conference of fruct association. Nov 5-8, 2019, Helsinki, Finland, pp. 1-9, 2019.
[21]
T. Ragupathi, and M. Govindarajan, "Performance assessment of different machine learning algorithms for medical decision support systems", Proceeding of the international conference on computer networks, big data and iot. Dec 19-20, 2019, Madurai, India, pp. 941-947, 2019.
[22]
T. Murari, L. Prathiba, and K. Kumar Singamaneni, " et al., Big data analytics with oenn based clinical decision support system", Intelligent Automation & Soft Computing, vol. 31 2022, no. 2, pp. 1241-1256, .
[23]
A.S. Hasan, H.M. Kader, and A. Hossam, "An intelligent detection system for COVID-19 diagnosis using ct-images", J.E.S., vol. 49, no. 4, pp. 476-508, 2021.
[http://dx.doi.org/10.21608/jesaun.2021.61028.1031]
[24]
A. Karthikeyan, A. Garg, P.K. Vinod, and U.D. Priyakumar, "Machine learning based clinical decision support system for early COVID-19 mortality prediction", Front. Public Health, vol. 9, p. 626697, 2021.
[http://dx.doi.org/10.3389/fpubh.2021.626697] [PMID: 34055710]
[25]
M.R. Afrash, M. Yaghoubi, F. Rahimi, M. Shanbehzadeh, and M. Bahadori, "An intelligent system for prediction of severity of sarscov-2 infection and progression to critical illness: Using machine learning models", Researchgate.
[26]
R. Moore, K.R. Archer, and L. Choi, "Statistical and machine learning models for classification of human wear and delivery days in accelerometry data", Sensors, vol. 21, no. 8, p. 2726, 2021.
[http://dx.doi.org/10.3390/s21082726] [PMID: 33924388]
[27]
H.H. Alalawi, "Detection of cardiovascular disease using machine learning classification models", IJERT, vol. 10, no. 7, 2021.
[28]
M. Ganesan, and N.R. Sivakumar, "IoT based heart disease prediction and diagnosis model for healthcare using machine learning models", IEEE international conference on system, computation, automation and networking (ICSCAN)..
March 29-30, 2019, Pondicherry, India, pp. 1-5, 2019. [http://dx.doi.org/10.1109/ICSCAN.2019.8878850]
[29]
S. Mishra, K. Shaw, and D. Mishra, "et al., Improving the accuracy of ensemble machine learning classification models using a novel bit-fusion algorithm for healthcare ai systems", Front. Public Health, vol. 10, p. 858282, 2022.
[http://dx.doi.org/10.3389/fpubh.2022.858282] [PMID: 35602150]
[30]
A. Nikam, S. Bhandari, A. Mhaske, and S. Mantri, "Cardiovascular disease prediction using machine learning models", IEEE Pune Section International Conference.
Dec 16-18, 2020, Pune, India, pp. 22-27, 2020. [http://dx.doi.org/10.1109/PuneCon50868.2020.9362367]
[31]
H. Mattie, P.J. Reidy, P. Bachtiger, E.R. Lindemer, M. Jouni, and T. Panch, "A framework for predicting impactability of healthcare interventions using machine learning methods, administrative claims, sociodemographic and app generated data", Arxiv, 2019.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy