Nothing Special   »   [go: up one dir, main page]

Skip to main content

Advertisement

Log in

A mathematical model based on modified ID3 algorithm for healthcare diagnostics model

  • ORIGINAL ARTICLE
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

Data has become an asset in this digitization revolution, and the healthcare segment is the leading cause of this big data. Healthcare data analysis is an influential and powerful source for developing new visions to upsurge attentiveness to well-being. Data in the healthcare sector consists of various symptoms, treatments, disease information, patient information, and, lastly, tests to detect diseases. Healthcare information, along with machine learning (ML) Algorithms, supports the examination of big data to identify and discover the hidden patterns in any condition which can be used to predict any disease. This paper proposes a decision-support framework for any disease prediction in the healthcare sector. This work proposed an Improvised ID3 Algorithm (Modified ID3) which is based on a simple model of decision tree algorithm (ID3) to reduce time complexity and complex computation through the application of arithmetic operations for entropy computation and obtaining information. The Modified ID3 algorithm is implemented in python programming by using a reduced feature set of the Hepatitis C virus dataset (Hoffmann et al. in J Lab Precis Med 3:58, 2018) along with standard ML algorithms, such as ID3, support vector machine, random forest, and other recent states of artwork. The proficiency of this work and other ML algorithms are tested via a confusion matrix for various assessment parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Akay A, Dragomir A, Erlandsson BE (2014) Network-based modeling and intelligent data mining of social media for improving care. IEEE J Biomed Health Inform 19(1):210–218

    Article  Google Scholar 

  • Arif F, Suryana N, Hussin B (2013) Cascade quality prediction method using multiple PCA+ ID3 for multi-stage manufacturing system. IERI Procedia 4:201–207

    Article  Google Scholar 

  • Baitharu TR, Pani SK (2016) Analysis of data mining techniques for healthcare decision support system using liver disorder dataset. Procedia Comput Sci 85:862–870

    Article  Google Scholar 

  • Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

    Article  MATH  Google Scholar 

  • Capizzi G, Coco S, Sciuto GL, Napoli C, Hołubowski W (2019) An entropy evaluation algorithm to improve transmission efficiency of compressed data in pervasive healthcare mobile sensor networks. IEEE Access 8:4668–4678

    Article  Google Scholar 

  • Castaldo R, Melillo P, Izzo R, De Luca N, Pecchia L (2016) Fall prediction in hypertensive patients via short-term HRV analysis. IEEE J Biomed Health Inform 21(2):399–406

    Article  Google Scholar 

  • Chen M, Hao Y, Hwang K, Wang L, Wang L (2017) Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5:8869–8879

    Article  Google Scholar 

  • Cheng YT, Lin YF, Chiang KH, Tseng VS (2017) Mining sequential risk patterns from large-scale clinical databases for early assessment of chronic diseases: a case study on chronic obstructive pulmonary disease. IEEE J Biomed Health Inform 21(2):303–311

    Google Scholar 

  • Coleman JN, Chester EI, Softley CI, Kadlec J (2000) Arithmetic on the European logarithmic microprocessor. IEEE Trans Comput 49(7):702–715

    Article  Google Scholar 

  • De Mántaras RL (1991) A distance-based attribute selection measure for decision tree induction. Mach Learn 6(1):81–92

    Article  Google Scholar 

  • Elhadjamor EA, Ghannouchi SA (2019) Analyze in depth health care business process and key performance indicators using process mining. Procedia Comput Sci 164:610–617

    Article  Google Scholar 

  • Forkan ARM, Khalil I, Ibaida A, Tari Z (2015) BDCaM: big data for context-aware monitoring—a personalized knowledge discovery framework for assisted healthcare. IEEE Trans Cloud Comput 5(4):628–641

    Article  Google Scholar 

  • Forsberg D, Rosipko B, Sunshine JL (2016) Analyzing PACS usage patterns by means of process mining: steps toward a more detailed workflow analysis in radiology. J Digit Imaging 29(1):47–58

    Article  Google Scholar 

  • Hajihashemi Z, Popescu M (2015) A multidimensional time-series similarity measure with applications to eldercare monitoring. IEEE J Biomed Health Inform 20(3):953–962

    Article  Google Scholar 

  • Haq AU, Li JP, Khan J, Memon MH, Nazir S, Ahmad S, Ali A (2020) Intelligent machine learning approach for effective recognition of diabetes in E-healthcare using clinical data. Sensors 20(9):2649

    Article  Google Scholar 

  • Hoffmann G, Bietenbeck A, Lichtinghagen R, Klawonn F (2018) Using machine learning techniques to generate laboratory diagnostic pathways—a case study. J Lab Precis Med 3:58

    Article  Google Scholar 

  • Ismail WN, Hassan MM, Alsalamah HA (2018) Mining of productive periodic-frequent patterns for IoT data analytics. Futur Gener Comput Syst 88:512–523

    Article  Google Scholar 

  • Jain K, Kumar A (2020) An energy-efficient prediction model for data aggregation in sensor network. J Ambient Intell Humaniz Comput 11(11):5205–5216

    Article  Google Scholar 

  • Jain K, Kumar A (2021) ST-DAM: exploiting spatial and temporal correlation for energy-efficient data aggregation method in heterogeneous WSN. Int J Wirel Mob Comput 21(3):285–294

    Article  Google Scholar 

  • Jain K, Singh A (2021) An empirical cluster head selection and data aggregation scheme for a fault-tolerant sensor network. Int J Distrib Syst Technol (IJDST) 12(3):27–47

    Article  Google Scholar 

  • Jain K, Gupta M, Abraham A (2021) A review on privacy and security assessment of cloud computing. J Inf Assur Secur 16(5):161–168

    Google Scholar 

  • Jain K, Singh A, Singh P, Yadav S (2022) An improved supervised classification algorithm in healthcare diagnostics for predicting opioid habit disorder. Int J Reliab Qual E-Healthc (IJRQEH) 11(1):1–16

    Article  Google Scholar 

  • Jin J, Sun W, Al-Turjman F, Khan MB, Yang X (2020) Activity pattern mining for healthcare. Ieee Access 8:56730–56738

    Article  Google Scholar 

  • Kibria MG, Nguyen K, Villardi GP, Zhao O, Ishizu K, Kojima F (2018) Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE Access 6:32328–32338

    Article  Google Scholar 

  • Kumar A, Srivastav AL, Dutt I, Bajaj K (2021) Classification of existing health model of india at the end of the twelfth plan using enhanced decision tree algorithm. Pertanika J Sci Technol. https://doi.org/10.47836/pjst.29.4.06

    Article  Google Scholar 

  • Leung CS, Wong KW, Sum PF, Chan LW (2001) A pruning method for the recursive least squared algorithm. Neural Netw 14(2):147–174

    Article  Google Scholar 

  • Li Y, Bai C, Reddy CK (2016) A distributed ensemble approach for mining healthcare data under privacy constraints. Inf Sci 330:245–259

    Article  Google Scholar 

  • Puppala M, He T, Chen S, Ogunti R, Yu X, Li F, Wong ST (2015) METEOR: an enterprise health informatics environment to support evidence-based medicine. IEEE Trans Biomed Eng 62(12):2776–2786

    Article  Google Scholar 

  • Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106

    Article  Google Scholar 

  • Raghuvanshi KK, Agarwal A, Jain K, Singh VB (2021) A generalized prediction model for improving software reliability using time-series modelling. Int J Syst Assur Eng Manag 13:1309

    Article  Google Scholar 

  • Raj RJS, Shobana SJ, Pustokhina IV, Pustokhin DA, Gupta D, Shankar K (2020) Optimal feature selection-based medical image classification using deep learning model in internet of medical things. IEEE Access 8:58006–58017

    Article  Google Scholar 

  • Rejab FB, Nouira K, Trabelsi A (2014) Health monitoring systems using machine learning techniques. Intelligent systems for science and information. Springer, Cham, pp 423–440

    Chapter  Google Scholar 

  • Rojas E, Munoz-Gama J, Sepúlveda M, Capurro D (2016) Process mining in healthcare: a literature review. J Biomed Inform 61:224–236

    Article  Google Scholar 

  • Shi Z, Zuo W, Liang S, Zuo X, Yue L, Li X (2020) IDDSAM: an integrated disease diagnosis and severity assessment model for intensive care units. IEEE Access 8:15423–15435

    Article  Google Scholar 

  • Suresh A, Udendhran R, Balamurgan M, Varatharajan R (2019) A novel internet of things framework integrated with real time monitoring for intelligent healthcare environment. J Med Syst 43(6):1–10

    Article  Google Scholar 

  • Xiong Y, Lu Y (2020) Deep feature extraction from the vocal vectors using sparse autoencoders for Parkinson’s classification. IEEE Access 8:27821–27830

    Article  Google Scholar 

  • Zhang Y (2012) Support vector machine classification algorithm and its application. In: International conference on information computing and applications. Springer, Berlin, Heidelberg, pp 179–186

Download references

Funding

This research received no specific grant from any funding agency.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Khushboo Jain.

Ethics declarations

Conflict of interest

The authors have no conflict of interest.

Human and animal participants

This work does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agarwal, A., Jain, K. & Yadav, R.K. A mathematical model based on modified ID3 algorithm for healthcare diagnostics model. Int J Syst Assur Eng Manag 14, 2376–2386 (2023). https://doi.org/10.1007/s13198-023-02086-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-023-02086-w

Keywords

Navigation