Skip to main content

Advertisement

SpringerLink
Log in

CNN with machine learning approaches using ExtraTreesClassifier and MRMR feature selection techniques to detect liver diseases on cloud

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Liver disease is a significant global burden on health, with about a few hundred million people suffering from chronic liver disease (CLD), with approximately 2 million deaths each year. Liver diseases are tough to identify and usually ignored in the early stages as it does not show any symptoms. The liver disease diagnosis in the early stage will help to take precautions to prevent future illness. Generally, recognition of people with liver illness is accomplished via liver biopsy and visual assessment of MRI by experienced professionals, which is a laborious and time-consuming practice. As a result, there is a need for the development of an automated detection method that can offer results with minimal and greater precision. The primary motivation of this work is to implement a machine learning (ML) based real-time liver diseases classification framework onto the cloud to reduce clinicians’ burden. The Indian Liver Patient Dataset (ILPD) was applied to classify liver diseases. The dataset has eleven attributes or features employed to train the models. The Convolutional Neural Network (CNN) was implemented and then the flatten layer output was given to the Logistic regression (LR), Random Forest (RF), and Support Vector Machine (SVM) classifier and achieved a precision of 100% for all models. The ExtraTreesClassifier (ETC) and Maximum Relevance Minimum Redundancy (MRMR) techniques were applied to select the features extracted by CNN and achieved remarkable 100% precision. The stratified K-fold method was used to evaluate the model performance. The comparative results confirm that the CNN-RF outperforms the literature-reported models. After the evaluation, the model was deployed successfully to the Platform-as-a-Service (PaaS) cloud.

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

Similar content being viewed by others

Data Availability

Not applicable.

References

  1. Acharya, U.R., Koh, J.E.W., Hagiwara, Y., Tan, J.H., Gertych, A., Vijayananthan, A., Yaakup, N.A., Abdullah, B.J.J., Bin Mohd Fabell, M.K., Yeong, C.H.: Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features. Computers in Biology and Medicine. 94, 11–18 (2018). https://doi.org/10.1016/j.compbiomed.2017.12.024

    Article  Google Scholar 

  2. Shahabi, M., Hassanpour, H., Mashayekhi, H.: Rule extraction for fatty liver detection using neural networks. Neural Comput. & Applic. 31, 979–989 (2019). https://doi.org/10.1007/s00521-017-3130-5

    Article  Google Scholar 

  3. Ali, L., Wajahat, I., Amiri Golilarz, N., Keshtkar, F., Bukhari, S.A.C.: LDA–GA–SVM: improved hepatocellular carcinoma prediction through dimensionality reduction and genetically optimized support vector machine. Neural Comput. & Applic. 33, 2783–2792 (2021). https://doi.org/10.1007/s00521-020-05157-2

    Article  Google Scholar 

  4. Grissa, D., Nytoft Rasmussen, D., Krag, A., Brunak, S., Juhl Jensen, L.: Alcoholic liver disease: A registry view on comorbidities and disease prediction. PLoS Comput. Biol. 16, e1008244 (2020). https://doi.org/10.1371/journal.pcbi.1008244

    Article  Google Scholar 

  5. Hashem, S., ElHefnawi, M., Habashy, S., El-Adawy, M., Esmat, G., Elakel, W., Abdelazziz, A.O., Nabeel, M.M., Abdelmaksoud, A.H., Elbaz, T.M., Shousha, H.I.: Machine Learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease. Comput. Methods Programs Biomed. 196, 105551 (2020). https://doi.org/10.1016/j.cmpb.2020.105551

    Article  Google Scholar 

  6. Losic, B., Craig, A.J., Villacorta-Martin, C., Martins-Filho, S.N., Akers, N., Chen, X., Ahsen, M.E., von Felden, J., Labgaa, I., DʹAvola, D., Allette, K., Lira, S.A., Furtado, G.C., Garcia-Lezana, T., Restrepo, P., Stueck, A., Ward, S.C., Fiel, M.I., Hiotis, S.P., Gunasekaran, G., Sia, D., Schadt, E.E., Sebra, R., Schwartz, M., Llovet, J.M., Thung, S., Stolovitzky, G., Villanueva, A.: Intratumoral heterogeneity and clonal evolution in liver cancer. Nat. Commun. 11, 291 (2020). https://doi.org/10.1038/s41467-019-14050-z

    Article  Google Scholar 

  7. Naseem, R., Khan, B., Shah, M.A., Wakil, K., Khan, A., Alosaimi, W., Uddin, M.I., Alouffi, B.: Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome. Journal of Healthcare Engineering. 1–13 (2020). (2020). https://doi.org/10.1155/2020/6680002

  8. Goceri, E., Shah, Z.K., Layman, R., Jiang, X., Gurcan, M.N.: Quantification of liver fat: A comprehensive review. Computers in Biology and Medicine. 71, 174–189 (2016). https://doi.org/10.1016/j.compbiomed.2016.02.013

    Article  Google Scholar 

  9. Abdar, M., Yen, N.Y., Hung, J.C.-S.: Improving the Diagnosis of Liver Disease Using Multilayer Perceptron Neural Network and Boosted Decision Trees. J. Med. Biol. Eng. 38, 953–965 (2018). https://doi.org/10.1007/s40846-017-0360-z

    Article  Google Scholar 

  10. Perveen, S., Shahbaz, M., Keshavjee, K., Guergachi, A.: A Systematic Machine Learning Based Approach for the Diagnosis of Non-Alcoholic Fatty Liver Disease Risk and Progression. Sci. Rep. 8, 2112 (2018). https://doi.org/10.1038/s41598-018-20166-x

    Article  Google Scholar 

  11. Yang, J.D., Ahmed, F., Mara, K.C., Addissie, B.D., Allen, A.M., Gores, G.J., Roberts, L.R.: Diabetes Is Associated With Increased Risk of Hepatocellular Carcinoma in Patients With Cirrhosis From Nonalcoholic Fatty Liver Disease. Hepatology. 71, 907–916 (2020). https://doi.org/10.1002/hep.30858

    Article  Google Scholar 

  12. Muruganantham, B.: Liver Disease Prediction Using Classification Algorithms. Int. J. Adv. Sci. Technol. 29, 311–319 (2020)

    Google Scholar 

  13. Kececi, A., Yildirak, A., Ozyazici, K., Ayluctarhan, G., Agbulut, O., Zincir, I.: Implementation of machine learning algorithms for gait recognition. Eng. Sci. Technol. Int. J. 23, 931–937 (2020). https://doi.org/10.1016/j.jestch.2020.01.005

    Article  Google Scholar 

  14. Govindarajan, P., Soundarapandian, R.K., Gandomi, A.H., Patan, R., Jayaraman, P., Manikandan, R.: Classification of stroke disease using machine learning algorithms. Neural Comput. & Applic. 32, 817–828 (2020). https://doi.org/10.1007/s00521-019-04041-y

    Article  Google Scholar 

  15. Ramesh, D., Katheria, Y.S.: Ensemble method based predictive model for analyzing disease datasets: a predictive analysis approach. Health Technol. 9, 533–545 (2019). https://doi.org/10.1007/s12553-019-00299-3

    Article  Google Scholar 

  16. Godara, S.: Evaluation of Predictive Machine Learning Techniques as Expert Systems in Medical Diagnosis. IJST. 9, 1–14 (2016). https://doi.org/10.17485/ijst/2016/v9i10/87212

    Article  Google Scholar 

  17. Sanaj, M.S., Joe Prathap, P.M.: Nature inspired chaotic squirrel search algorithm (CSSA) for multi objective task scheduling in an IAAS cloud computing atmosphere. Eng. Sci. Technol. Int. J. 23, 891–902 (2020). https://doi.org/10.1016/j.jestch.2019.11.002

    Article  Google Scholar 

  18. Tanwar, N., Rahman, K.F.: Machine Learning in liver disease diagnosis: Current progress and future opportunities. IOP Conf. Ser. : Mater. Sci. Eng. 1022, 012029 (2021). https://doi.org/10.1088/1757-899X/1022/1/012029

    Article  Google Scholar 

  19. Jaganathan, K., Tayara, H., Chong, K.T.: Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets. IJMS. 22, 8073 (2021). https://doi.org/10.3390/ijms22158073

    Article  Google Scholar 

  20. Thirunavukkarasu, Singh, A.S., Irfan, M., Chowdhury, A.: Prediction of Liver Disease using Classification Algorithms. In: 2018 4th International Conference on Computing Communication and Automation (ICCCA). pp. 1–3 (2018)

  21. Razali, N., Mustapha, A., Wahab, M.H.A., Mostafa, S.A., Rostam, S.K.: A Data Mining Approach to Prediction of Liver Diseases. J. Phys. : Conf. Ser. 1529, 032002 (2020). https://doi.org/10.1088/1742-6596/1529/3/032002

    Article  Google Scholar 

  22. Ayeldeen, H., Shaker, O., Ayeldeen, G., Anwar, K.M.: Prediction of liver fibrosis stages by machine learning model: A decision tree approach. In: 2015 Third World Conference on Complex Systems (WCCS). pp. 1–6 (2015)

  23. Belavigi, D.H., Veena, G.S., Harekal, D.: Prediction of Liver Disease using Rprop, SAG and CNN. Int. J. Innovative Technol. Exploring Eng. (IJITEE). 8, 8 (2019)

    Google Scholar 

  24. Kumar, S., Katyal, S.: Effective Analysis and Diagnosis of Liver Disorder by Data Mining. In: 2018 International Conference on Inventive Research in Computing Applications (ICIRCA). pp. 1047–1051 (2018)

  25. Hashem, S., Esmat, G., Elakel, W., Habashy, S., Raouf, S.A., Elhefnawi, M., Eladawy, M.I., ElHefnawi, M.: Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients. IEEE/ACM Trans. Comput. Biol. and Bioinf. 15, 861–868 (2018). https://doi.org/10.1109/TCBB.2017.2690848

    Article  Google Scholar 

  26. Vats, V., Zhang, L., Chatterjee, S., Ahmed, S., Enziama, E., Tepe, K.: A Comparative Analysis of Unsupervised Machine Techniques for Liver Disease Prediction. In: 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). pp. 486–489 (2018)

  27. Assistant, Professor, Department of Computer Science, Thiruvalluvar University College of Arts and Science, Thennangur, V., Kuppan, P., Manoharan, N.: Head and Assistant Professor, Department of Computer Science, Thiruvalluvar University College of Arts and Science, Thennangur,Vandavasi: A Tentative analysis of Liver Disorder using Data mining Algorithms J48, Decision Table and Naive Bayes. IJCOA. 6, 37–40 (2017). https://doi.org/10.20894/IJCOA.101.006.001.009

    Article  Google Scholar 

  28. Department of Information Technology, University, B.Z., Pakistan, Pasha, M., Fatima, M.: Comparative Analysis of Meta Learning Algorithms for Liver Disease Detection. JSW. 12, 923–933 (2017). https://doi.org/10.17706/jsw.12.12.923-933

    Article  Google Scholar 

  29. Baitharu, T.R., Pani, S.K.: Procedia Comput. Sci. 85, 862–870 (2016). https://doi.org/10.1016/j.procs.2016.05.276 Analysis of Data Mining Techniques for Healthcare Decision Support System Using Liver Disorder Dataset

  30. Sontakke, S., Lohokare, J., Dani, R.: Diagnosis of liver diseases using machine learning. In: 2017 International Conference on Emerging Trends Innovation in ICT (ICEI). pp. 129–133 (2017)

  31. Singh, G., Agarwal, C., Gupta, S.: Detection of Liver Disease Using Machine Learning Techniques: A Systematic Survey. In: Balas, V.E., Sinha, G.R., Agarwal, B., Sharma, T.K., Dadheech, P., Mahrishi, M. (eds.) Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT, pp. 39–51. Springer International Publishing, Cham (2022)

    Google Scholar 

  32. Pasha, S.N., Ramesh, D., Mohmmad, S., Kishan, P.N., Sandeep, P.A.: C.H.: Liver disease prediction using ML techniques. Presented at the INTERNATIONAL CONFERENCE ON RESEARCH IN SCIENCES, ENGINEERING & TECHNOLOGY, Warangal, India (2022)

  33. Poonguzharselvi, B.: M.M.A.A.: Prediction of Liver Disease Using Machine Learning Algorithm and Genetic Algorithm.Annals of the Romanian Society for Cell Biology.2347–2357(2021)

  34. Yajurved, J., Prasad, P.S., Km, D.U.: Analysis of Chronic Disease (Liver) Prediction Using Machine Learning.Journal of Positive School Psychology.5489–5496(2022)

  35. Keerthana, P.S.M., Phalinkar, N., Mehere, R., Bhanu Prakash Reddy, K., Lal, N.: A Prediction Model of Detecting Liver Diseases in Patients using Logistic Regression of Machine Learning. Social Science Research Network, Rochester, NY (2020)

    Book  Google Scholar 

  36. Abdar, M., Zomorodi-Moghadam, M., Das, R., Ting, I.-H.: Performance analysis of classification algorithms on early detection of liver disease. Expert Syst. Appl. 67, 239–251 (2017). https://doi.org/10.1016/j.eswa.2016.08.065

    Article  Google Scholar 

  37. UCI Machine Learning Repository: : Data Set, https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset

  38. Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R.P., Tang, J., Liu, H.: Feature Selection: A Data Perspective. ACM Comput. Surv. 50, 1–45 (2018). https://doi.org/10.1145/3136625

    Article  Google Scholar 

  39. Research, Scholar, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India., Latha, P.H., Mohanasundaram, R., Professor, A., School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India.: A New Hybrid Strategy for Malware Detection Classification with Multiple Feature Selection Methods and Ensemble Learning Methods. IJEAT. 9, 4013–4018 (2019). https://doi.org/10.35940/ijeat.B4666.129219

  40. Bhandari, N.: ExtraTreesClassifier, (2018). https://medium.com/@namanbhandari/extratreesclassifier-8e7fc0502c7,

  41. ML | Extra Tree Classifier for Feature Selection:, (2019). https://www.geeksforgeeks.org/ml-extra-tree-classifier-for-feature-selection/,

  42. 9 Feature Transformation & Scaling: Techniques| Boost Model Performance, (2020). https://www.analyticsvidhya.com/blog/2020/07/types-of-feature-transformation-and-scaling/,

  43. Radovic, M., Ghalwash, M., Filipovic, N., Obradovic, Z.: Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinf. 18, 9 (2017). https://doi.org/10.1186/s12859-016-1423-9

    Article  Google Scholar 

  44. Aslan, S.N., Özalp, R., Uçar, A., Güzeliş, C.: New CNN and hybrid CNN-LSTM models for learning object manipulation of humanoid robots from demonstration. Cluster Comput. 25, 1575–1590 (2022). https://doi.org/10.1007/s10586-021-03348-7

    Article  Google Scholar 

  45. Lanjewar, M.G., Gurav, O.L.: Convolutional Neural Networks based classifications of soil images. Multimed Tools Appl. 81, 10313–10336 (2022). https://doi.org/10.1007/s11042-022-12200-y

    Article  Google Scholar 

  46. Lanjewar, M.G., Morajkar, P.P., Parab, J.: Detection of tartrazine colored rice flour adulteration in turmeric from multi-spectral images on smartphone using convolutional neural network deployed on PaaS cloud. Multimed Tools Appl. 81, 16537–16562 (2022). https://doi.org/10.1007/s11042-022-12392-3

    Article  Google Scholar 

  47. Brownlee, J.: How to Use StandardScaler and MinMaxScaler Transforms in Python, (2020). https://machinelearningmastery.com/standardscaler-and-minmaxscaler-transforms-in-python/,

  48. Understanding, L., Regression, (2017). https://www.geeksforgeeks.org/understanding-logistic-regression/,

  49. Lanjewar, M.G., Parate, R.K., Parab, J.S.: Machine Learning Approach with Data Normalization Technique for Early Stage Detection of Hypothyroidism. In: Artificial Intelligence Applications for Health Care, pp. 91–108. CRC Press (2022)

  50. Pant, A.: Introduction to Logistic Regression, https://towardsdatascience.com/introduction-to-logistic-regression-66248243c148

  51. Karagül Yıldız, T., Yurtay, N., Öneç, B.: Classifying anemia types using artificial learning methods. Eng. Sci. Technol. Int. J. 24, 50–70 (2021). https://doi.org/10.1016/j.jestch.2020.12.003

    Article  Google Scholar 

  52. Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27, 861–874 (2006). https://doi.org/10.1016/j.patrec.2005.10.010

    Article  Google Scholar 

  53. Chidambaram, S., Srinivasagan, K.G.: Performance evaluation of support vector machine classification approaches in data mining. Cluster Comput. 22, 189–196 (2019). https://doi.org/10.1007/s10586-018-2036-z

    Article  Google Scholar 

  54. Priya, M., Juliet, P., Tamilselvi, P.: Performance Analysis of Liver Disease Prediction Using Machine Learning Algorithms, https://www.semanticscholar.org/paper/Performance-Analysis-of-Liver-Disease-Prediction-Priya-Juliet/d5bd2f34087fd9e4de29eb6cff328f7bc5e63b20

  55. Pathan, A.: Comparative Study of Different Classification Algorithms on ILPD Dataset to Predict Liver Disorder. IJRASET. 6, 388–394 (2018). https://doi.org/10.22214/ijraset.2018.2056

    Article  Google Scholar 

  56. Muthuselvan, S., Rajapraksh, S., Somasundaram, K., Karthik, K.: Classification of Liver Patient Dataset Using Machine Learning Algorithms. IJET. 7, 323 (2018). https://doi.org/10.14419/ijet.v7i3.34.19217

    Article  Google Scholar 

  57. Kaur, H., Bhalla, S., Raghava, G.P.S.: Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles. PLoS ONE. 14, e0221476 (2019). https://doi.org/10.1371/journal.pone.0221476

    Article  Google Scholar 

  58. Shaker Abdalrada, A., Hashim Yahya, O., Hadi, M., Alaidi, A., Ali Hussein, N., Alrikabi, T.H., Al-Quraishi, H.: A Predictive model for liver disease progression based on logistic regression algorithm. PEN. 7, 1255 (2019). https://doi.org/10.21533/pen.v7i3.667

    Article  Google Scholar 

  59. Harshpreet Kaur, G.S.: The Diagnosis of Chronic Liver Disease using Machine Learning Techniques. ITII. 9, 554–564 (2021). https://doi.org/10.17762/itii.v9i2.382

    Article  Google Scholar 

  60. Dattatreya, P., Mankame, Harshitha, R., Navya, N.C., Nitin Ravichander, Machine Learning Techniques in Analysis and Prediction of Liver Disease,IJIRT,Volume8, Issue 2, (2022)

  61. Mostafa, F., Hasan, E., Williamson, M., Khan, H.: Statistical Machine Learning Approaches to Liver Disease Prediction. Livers. 1, 294–312 (2021). https://doi.org/10.3390/livers1040023

    Article  Google Scholar 

  62. Joloudari, J.H., Saadatfar, H., Dehzangi, A., Shamshirband, S.: Computer-aided decision-making for predicting liver disease using PSO-based optimized SVM with feature selection. Inf. Med. Unlocked. 17, 100255 (2019). https://doi.org/10.1016/j.imu.2019.100255

    Article  Google Scholar 

  63. Singh, J., Bagga, S., Kaur, R.: Software-based Prediction of Liver Disease with Feature Selection and Classification Techniques. Procedia Comput. Sci. 167, 1970–1980 (2020). https://doi.org/10.1016/j.procs.2020.03.226

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. School of Physical and Applied Sciences, Goa University, Taleigao Plateau, 403206, Goa, India

    Madhusudan G Lanjewar, Jivan S Parab, Arman Yusuf Shaikh & Marlon Sequeira

Authors
  1. Madhusudan G Lanjewar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jivan S Parab
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arman Yusuf Shaikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marlon Sequeira
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Not applicable.

Corresponding authors

Correspondence to Madhusudan G Lanjewar or Marlon Sequeira.

Ethics declarations

Conflicts of interest

We have no conflicts of interest to disclose

Research involving human participants and/or animals

Not applicable.

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 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

Lanjewar, M.G., Parab, J.S., Shaikh, A.Y. et al. CNN with machine learning approaches using ExtraTreesClassifier and MRMR feature selection techniques to detect liver diseases on cloud. Cluster Comput 26, 3657–3672 (2023). https://doi.org/10.1007/s10586-022-03752-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-022-03752-7

Keywords

Search

Navigation