Skip to main content

Advertisement

SpringerLink
Log in

A hybrid approach for medical images classification and segmentation to reduce complexity

  • S.I. : Intelligence for Systems and Software Engineering
  • Published:
Innovations in Systems and Software Engineering Aims and scope Submit manuscript

Abstract

The computational domain facilitates the performance of novel and innovative medical research and development tasks by providing support and computational power. This analysis method forecasts the future by analyzing the data we now have. The method may be divided into three primary phases: preprocessing, feature extraction, and classification. The research presented here aimed to improve the precision with which heart disease could be predicted across three distinct phases. The first step is thoroughly examining the databases kept at the UCI computer repository. In this study, we use the dataset’s five different algorithms, decision tree, Naive Bayes, random forest, KNN, and support vector machine, to compare their respective performances. In addition to age, the suggested revolutionary technique considers other characteristics such as pulse rate, cholesterol, and so on, which was not the case in earlier studies. In the past, age was the primary consideration in analysis and illness prediction. Compared to more traditional methods, improved prediction accuracy is achieved by modifying the study’s primitive properties. Third, this research introduced a novel hybrid classification model by fusing support vector machines and k-nearest neighbor classification techniques. A k-nearest neighbor classifier will do the heavy lifting to classify the data, while support vector machines will extract the dataset’s features. The accuracy rates for the various prediction methods decision tree, KNN, Naive Bayes, random forest, support vector machine, and proposed method range from 72.53% to 87.32% to 87.39% to 81.34%, respectively. The new technique decreases execution value by 5 % and increases accuracy by up to 8 %. The suggested model outperforms state-of-the-art approaches in terms of accuracy and implementation speed.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Yuan X, Chen J, Zhang K, Wu Y, Yang T (2022) A stable AI-based binary and multiple class heart disease prediction model for IoMT. IEEE Trans Industr Inf 18(3):2032–2040. https://doi.org/10.1109/TII.2021.3098306

    Article  Google Scholar 

  2. Sheeba A, Padmakala S, Subasini CA, Karuppiah SP (2022) MKELM: Mixed kernel extreme learning machine using BMDA optimization for web services based heart disease prediction in smart healthcare. Comput Methods Biomech Biomed Eng 25(10):1180–1194. https://doi.org/10.1080/10255842.2022.2034795

    Article  Google Scholar 

  3. Dhasaradhan K, Jaichandran R (2022) Performance analysis of machine learning algorithms in heart disease prediction. Concurr Eng-Res Appl. https://doi.org/10.1177/1063293X221125231

    Article  Google Scholar 

  4. Fazlur SAH, Thillaigovindan SK (2022) Integrated deep learning model for heart disease prediction using variant medical data sets. Int J Online Biomed Eng 18(9):178–191. https://doi.org/10.3991/ijoe.v18i09.30801

    Article  Google Scholar 

  5. Shea S, Blaha MJ (2022) Long-term risk prediction for heart failure, disparities, and early prevention. Circ Res 130(2):210–212. https://doi.org/10.1161/CIRCRESAHA.121.320598

    Article  Google Scholar 

  6. Al Bataineh A, Manacek S (2022) MLP-PSO hybrid algorithm for heart disease prediction. J Personal Med. https://doi.org/10.3390/jpm12081208

    Article  Google Scholar 

  7. El-Shafiey MG, Hagag A, El-Dahshan E-SA, Ismail MA (2022) A hybrid GA and PSO optimized approach for heart-disease prediction based on random forest. Multimed Tools Appl 81(13):18155–18179. https://doi.org/10.1007/s11042-022-12425-x

    Article  Google Scholar 

  8. Kampaltsis P, Emfiezoglou M, Siouras A, Eynde J, Moustakidis S, Doulamis IP, Duque ER, Briasoulis A (2022) Prediction of 1-year mortality after heart transplantation in adults with congenital heart disease with machine learning models. J Heart Lung Transpl 41(4, S):436

    Article  Google Scholar 

  9. Amer SS, Wander G, Singh M, Bahsoon R, Jennings NR, Gill SS (2022) Biolearner: a machine learning-powered smart heart disease risk prediction system utilizing biomedical markers. J Interconnect Netw. https://doi.org/10.1142/S0219265921450031

    Article  Google Scholar 

  10. Mete M, Ayvaci MUS, Ariyamuthu VK, Amin A, Peltz M, Thibodeau JT, Grodin JL, Mammen PPA, Garg S, Araj F, Morlend R, Drazner MH, AbdulRahim N, Kim Y, Salam Y, Gungor AB, Delibasi B, Kotla SK, MacConmara MP, Anand PM, Gupta G, Tanriover B (2022) Predicting post-heart transplant composite renal outcome risk in adults: a machine learning decision tool. Kidney Int Rep 7(6):1410–1415. https://doi.org/10.1016/j.ekir.2022.04.004

    Article  Google Scholar 

  11. Kumar PR, Ravichandran S, Narayana S (2022) Optimization assisted hybrid intelligent system for heart disease prediction. J Mech Med Biol. https://doi.org/10.1142/S0219519422500518

    Article  Google Scholar 

  12. Jesi VE, Aslam SM (2022) An intelligent disease prediction and monitoring system using feature selection, multi-neural network and fuzzy rules. Neural Comput Appl. https://doi.org/10.1007/s00521-022-07527-4

    Article  Google Scholar 

  13. Dileep P, Rao KN, Bodapati P, Gokuruboyina S, Peddi R, Grover A, Sheetal A (2022) An automatic heart disease prediction using cluster-based bi-directional D(C-BiLSTM) algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-022-07064-0

    Article  Google Scholar 

  14. Masih N, Ahuja S (2022) Application of data mining techniques for early detection of heart diseases using Famingham heart study dataset. Int J Biomed Eng Technol 38(4):334–344

    Article  Google Scholar 

  15. Sedaghati-Khayat B, Bos M, Tan J, van Meurs JBJ, Uitterlinden A, Hajek C, Rotter JI, Kavousi M, van Rooij J (2022) Polygenic risk prediction ability of gender-stratified coronary heart disease. Eur J Hum Genet 30(Suppl 1,1):519

    Google Scholar 

  16. Absar N, Das EK, Shoma SN, Khandaker MU, Miraz MH, Faruque MRI, Tamam N, Sulieman A, Pathan RK (2022) The efficacy of machine-learning-supported smart system for heart disease prediction. Healthcare. https://doi.org/10.3390/healthcare10061137

    Article  Google Scholar 

  17. Black DR (2022) A new tool in the prediction of cardiovascular disease? perhaps. Menopause J North Am Menopause Soc 29(8):892–893. https://doi.org/10.1097/GME.0000000000002038

    Article  Google Scholar 

  18. Phasinam K, Mondal T, Novaliendry D, Yang C-H, Dutta C, Shabaz M (2022) Analyzing the performance of machine learning techniques in disease prediction. J Food Qual. https://doi.org/10.1155/2022/7529472

    Article  Google Scholar 

  19. Elhoff JJ (2022) Do we need to reframe our approach to short-term outcomes in the cardiac ICU? Pediatr Crit Care Med 23(2):140–143. https://doi.org/10.1097/PCC.0000000000002875

    Article  Google Scholar 

  20. Patil S, Pingle S-R, Shalaby K, Kim AS (2022) Mediastinal irradiation and valvular heart disease. Cardio-Oncol. https://doi.org/10.1186/s40959-022-00133-2

    Article  Google Scholar 

  21. Karthick K, Aruna SK, Samikannu R, Kuppusamy R, Teekaraman Y, Thelkar AR (2022) Implementation of a heart disease risk prediction model using machine learning. Comput Math Methods Med. https://doi.org/10.1155/2022/6517716

    Article  Google Scholar 

  22. Bai J, Fu J, Du S, Chen X, Zhang C (2022) Prediction of heart failure in children with congenital heart disease based on multichannel LSTM. Mob Inf Syst. https://doi.org/10.1155/2022/5901445

    Article  Google Scholar 

  23. Criqui MH, Bhatia HS (2022) How should we measure and score coronary artery calcium? JACC-Cardiovasc Imaging 15(3):501–503

    Article  Google Scholar 

  24. Alotaibi N, Alzahrani M (2022) Comparative analysis of machine learning algorithms and data mining techniques for predicting the existence of heart disease. Int J Adv Comput Sci Appl 13(7):810–818

    Google Scholar 

  25. Rashid J, Kanwal S, Kim J, Nisar MW, Naseem U, Hussain A (2022) Heart disease diagnosis using the brute force algorithm and machine learning techniques. CMC-Comput Mater Contin 72(2):3195–3211. https://doi.org/10.32604/cmc.2022.026064

    Article  Google Scholar 

  26. Azmi J, Arif M, Nafis MT, Alam MA, Tanweer S, Wang G (2022) A systematic review on machine learning approaches for cardiovascular disease prediction using medical big data. Med Eng Phys. https://doi.org/10.1016/j.medengphy.2022.103825

    Article  Google Scholar 

  27. Deepika D, Balaji N (2022) Effective heart disease prediction with grey-wolf with firefly algorithm-differential evolution (GF-DE) for feature selection and weighted ANN classification. Comput Methods Biomech Biomed Eng 25(12):1409–1427. https://doi.org/10.1080/10255842.2022.2078966

    Article  Google Scholar 

  28. Ahmad GN, Shafiullah, Fatima H, Abbas M, Rahman O, Imdadullah, Alqahtani MS (2022) Mixed machine learning approach for efficient prediction of human heart disease by identifying the numerical and categorical features. Appl Sci-Basel. https://doi.org/10.3390/app12157449

    Article  Google Scholar 

  29. Ganesh VM, Nithiyanantham J (2022) Heuristic-based channel selection with enhanced deep learning for heart disease prediction under WBAN. Comput Methods Biomech Biomed Eng. https://doi.org/10.1080/10255842.2021.2013828

    Article  Google Scholar 

  30. Ansarullah SI, Saif SM, Kumar P, Kirmani MM (2022) Significance of visible non-invasive risk attributes for the initial prediction of heart disease using different machine learning techniques. Comput Intell Neurosci. https://doi.org/10.1155/2022/9580896

    Article  Google Scholar 

  31. Archana KS, Sivakumar B, Kuppusamy R, Teekaraman Y, Radhakrishnan A (2022) Automated cardioailment identification and prevention by hybrid machine learning models. Comput Math Methods Med. https://doi.org/10.1155/2022/9797844

    Article  Google Scholar 

  32. Kannan S (2022) Modelling an efficient clinical decision support system for heart disease prediction using learning and optimization approaches. CMES-Comput Model Eng Sci 31(2):677–694. https://doi.org/10.32604/cmes.2022.018580

    Article  Google Scholar 

  33. Maulion C, Januzzi JL (2022) Risk prediction scores in cardiovascular disease: Useful tool or “model of the week’’? J Cardiac Fail 28(4):551–553. https://doi.org/10.1016/j.cardfail.2021.11.019

    Article  Google Scholar 

  34. Manimurugan S, Almutairi S, Aborokbah MM, Narmatha C, Ganesan S, Chilamkurti N, Alzaheb RA, Almoamari H (2022) Two-stage classification model for the prediction of heart disease using IoMT and artificial intelligence. Sensors. https://doi.org/10.3390/s22020476

  35. Leeson P, Nanayakkara S, Lamata P (2022) Editorial: translating artificial intelligence into clinical use within cardiology. Front Cardiovasc Med. https://doi.org/10.3389/fcvm.2022.995234

    Article  Google Scholar 

  36. Pan C, Poddar A, Mukherjee R, Ray AK (2022) Impact of categorical and numerical features in ensemble machine learning frameworks for heart disease prediction. Biomed Signal Process Control. https://doi.org/10.1016/j.bspc.2022.103666

    Article  Google Scholar 

Download references

Author information

Author notes

  1. Surbhi Bhatia, Rajat Bhardwaj, Kamred Udham Singh, Neeraj varshney and Linesh Raja have contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Engineering and Applications, GLA University, Chaumuhan, Mathura, Uttar Pradesh, 281406, India

    Ankit Kumar & Neeraj varshney

  2. Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, 31451, Dammam, Saudi Arabia

    Surbhi Bhatia

  3. Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh-Patiala National Highway, Rajpura, Punjab, 140401, India

    Rajat Bhardwaj

  4. Department of Computer Science and Information Engineering, National Cheng Kung University, Dasyue Street, Tainan, 701, Tainan, Taiwan

    Kamred Udham Singh

  5. School of Computing, Graphic Era Hill University, Bell Road, Dehradun, Uttarakhand, 248002, India

    Kamred Udham Singh

  6. Department of Computer Apllications, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India

    Linesh Raja

Authors
  1. Ankit Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Surbhi Bhatia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rajat Bhardwaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kamred Udham Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Neeraj varshney
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Linesh Raja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ankit Kumar.

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

Kumar, A., Bhatia, S., Bhardwaj, R. et al. A hybrid approach for medical images classification and segmentation to reduce complexity. Innovations Syst Softw Eng 19, 33–46 (2023). https://doi.org/10.1007/s11334-022-00512-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11334-022-00512-z

Keywords

Search

Navigation