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Optimized Extreme Learning Machine with Bacterial Colony Optimization Algorithm for Disease Diagnosis in Clinical Datasets

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Abstract

Currently, the computational disease diagnostic models are mainly for approximating the non-linear complex sensitive patterns in the medical data. For the most accurate diagnostic, it is to be processed by more sophisticated algorithms. The clinical data is obtained from various clinical tests conducted on both infected and healthy individuals. It is crucial to precisely identify the presence of sickness to provide treatment promptly. Disease detection models must accurately differentiate between true positive and negative outcomes. Manual interpretations may result in either false positives or false negatives. This study aims to create a universal framework for diagnosing diseases on clinical datasets. For this scenario, the Extreme Learning Machine (ELM) is combined with the Bacterial Colony Optimization (BCO) method to boost prediction accuracy and speed up global convergence. For efficient selection of the extreme learning machine model’s optimal weights and biases, it is recommended to use the BCO algorithm's neighborhood-based communication strategy. Eight different UCI medical datasets are used to analyze the performance of the developed optimized ELM method. The experimental results showed that the suggested BCO-ELM outperformed in terms of accuracy, sensitivity, specificity, precision, f-measure, ROC-AUC, convergence speed. It obtained approximately 5 to 10 percent maximal accuracy and f-measure than the existing traditional approaches and obtained an AUC of 0.9 to 1 on all datasets. The experimental results confirmed that the developed approach produced high classification accuracy when compared with other existing approaches.

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Data Availability

The used datasets are available in https://archive.ics.uci.edu/. WBDC: https://archive.ics.uci.edu/dataset/17/breast+cancer + wisconsin + diagnostic. WBPC: https://archive.ics.uci.edu/dataset/16/breast+cancer + wisconsin + prognostic. Thyroid: https://archive.ics.uci.edu/dataset/102/thyroid+disease. Heart Disease: https://archive.ics.uci.edu/dataset/45/heart+disease. Parkinson’s Disease Classification: https://archive.ics.uci.edu/dataset/470/parkinson+s+disease + classification. Fertility: https://archive.ics.uci.edu/dataset/244/fertility. MD: https://archive.ics.uci.edu/dataset/351/mesothelioma+s+disease + data + set. CC: https://archive.ics.uci.edu/dataset/537/cervical+cancer+behavior+risk.

References

  1. Kumari N, Acharjya D. Data classification using rough set and bioinspired computing in healthcare applications-an extensive review. Multimed Tools Appl. 2023;82(9):13479–505.

    Article  Google Scholar 

  2. Pawlovsky AP. An ensemble based on distances for a kNN method for heart disease diagnosis. In 2018 international conference on electronics, information, and communication (ICEIC). 2018. IEEE.

  3. Ilyas H, et al. Chronic kidney disease diagnosis using decision tree algorithms. BMC Nephrol. 2021;22(1):1–11.

    Article  Google Scholar 

  4. Asadi S, Roshan S, Kattan MW. Random forest swarm optimization-based for heart diseases diagnosis. J Biomed Inform. 2021;115: 103690.

    Article  Google Scholar 

  5. Bhagya Shree S, Sheshadri H. Diagnosis of Alzheimer’s disease using naive Bayesian classifier. Neural Comput Appl. 2018;29:123–32.

    Article  Google Scholar 

  6. Dolatabadi AD, Khadem SEZ, Asl BM. Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM. Comput Methods Programs Biomed. 2017;138:117–26.

    Article  Google Scholar 

  7. Chowdhury DR, Chatterjee M, Samanta R. An artificial neural network model for neonatal disease diagnosis. Int J Artif Intell Expert Syst (IJAE). 2011;2(3):96–106.

    Google Scholar 

  8. Chuang C-L. Case-based reasoning support for liver disease diagnosis. Artif Intell Med. 2011;53(1):15–23.

    Article  Google Scholar 

  9. Balaji E, et al. Automatic and non-invasive Parkinson’s disease diagnosis and severity rating using LSTM network. Appl Soft Comput. 2021;108: 107463.

    Article  Google Scholar 

  10. Ismaeel S, Miri A, Chourishi D. Using the extreme learning machine (ELM) technique for heart disease diagnosis. In: 2015 IEEE Canada international humanitarian technology conference (IHTC2015). 2015. IEEE.

  11. Gowri R, Rathipriya R. Protein motif comparator using PSO k-means. Int J Appl Metaheuristic Comput (IJAMC). 2016;7(3):56–68.

    Article  Google Scholar 

  12. Gowri R, Rathipriya R. Extraction of protein sequence motif information using PSO K-Means. arXiv preprint arXiv:1504.02235, 2015.

  13. Velusamy K, Manavalan R. Performance analysis of unsupervised classification based on optimization. Int J Comput Appl. 2012;42:22–7.

    Google Scholar 

  14. Gowri R, Sivabalan S, Rathipriya R. Biclustering using venus flytrap optimization algorithm. In: Computational intelligence in data mining—Volume 1: Proceedings of the international conference on CIDM, 5–6 December 2015. 2016. Springer.

  15. Gowri R, Rathipriya R. A novel cancer drug target module mining approach using nonswarm intelligence. In: Computational Intelligence in Cancer Diagnosis. Elsevier; 2023. p. 359–87.

    Chapter  Google Scholar 

  16. Gowri R, Rathipriya R. Non-swarm intelligence algorithms: a case study. Computing. 2021;103(8):1815–57.

    Article  Google Scholar 

  17. Gowri R, Rathipriya R. Non-swarm plant intelligence algorithm: BladderWorts suction (BWS) algorithm. In: 2018 international conference on circuits and systems in digital enterprise technology (ICCSDET). 2018. IEEE.

  18. Xiao C, Choi E, Sun J. Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. J Am Med Inform Assoc. 2018;25(10):1419–28.

    Article  Google Scholar 

  19. Prakash V, et al. An improved bacterial colony optimization using opposition-based learning for data clustering. Clust Comput. 2022;25(6):4009–25.

    Article  Google Scholar 

  20. Tamilarisi K, Gogulkumar M, and Velusamy K. Data clustering using bacterial colony optimization with particle swarm optimization. In: 2021 fourth international conference on electrical, computer and communication technologies (ICECCT). 2021. IEEE.

  21. Revathi J, Eswaramurthy V, Padmavathi P. Hybrid data clustering approaches using bacterial colony optimization and k-means. IOP Conf Ser: Mater Sci Eng. 2021. https://doi.org/10.1088/1757-899X/1070/1/012064.

    Article  Google Scholar 

  22. Revathi J, Eswaramurthy V, Padmavathi P. Bacterial colony optimization for data clustering. In: 2019 IEEE international conference on electrical, computer and communication technologies (ICECCT). 2019. IEEE.

  23. Babu SS, Jayasudha K. A simplex method-based bacterial colony optimization algorithm for data clustering analysis. Int J Pattern Recognit Artif Intell. 2022;36(12):2259027.

    Article  Google Scholar 

  24. Babu SS, Jayasudha K. A simplex method-based bacterial colony optimization for data clustering. In: Innovative data communication technologies and application: proceedings of ICIDCA 2021. 2022, Springer. p. 987–995

  25. Dhanalakshmi S, Sathiyabama S, Ayyamuthukumar D. A novel text clustering approach based on bacterial colony optimization. In: 2023 7th international conference on electronics, communication and aerospace technology (ICECA). 2023. IEEE.

  26. Vigneshvaran P, Kathiravan AV. Heart disease prediction using an optimized extreme learning machine with bacterial colony optimization. In: 2022 3rd international conference on smart electronics and communication (ICOSEC). 2022. IEEE.

  27. Vijayakumari K, Baby Deepa V. Fuzzy C-means hybrid with fuzzy bacterial colony optimization. In: International conference on advances in electrical and computer technologies. 2020. Springer.

  28. Zhou Z, Islam MT, Xing L. Multibranch CNN with MLP-Mixer-based feature exploration for high-performance disease diagnosis. IEEE Trans Neural Netw Learn Syst, 2023.

  29. Pahuja G, Nagabhushan T. A novel GA-ELM approach for Parkinson’s disease detection using brain structural T1-weighted MRI data. In: 2016 second international conference on cognitive computing and information processing (CCIP). 2016. IEEE.

  30. Shahid AH et al. Coronary artery disease diagnosis using feature selection based hybrid extreme learning machine. In: 2020 3rd international conference on information and computer technologies (ICICT). 2020. IEEE.

  31. Li J, et al. PSO-ELM optimization algorithm for gastroscopic image classification. In: 2022 16th ICME international conference on complex medical engineering (CME). 2022. IEEE.

  32. Nayak DR, Dash R, Majhi B. Discrete ripplet-II transform and modified PSO based improved evolutionary extreme learning machine for pathological brain detection. Neurocomputing. 2018;282:232–47.

    Article  Google Scholar 

  33. Kalaiselvi K, David VK. Modified extreme learning machine algorithm with deterministic weight modification for investment decisions based on sentiment analysis. Recent Adv Comput Sci Commun (Formerly: Recent Patents on Comput Sci. 2023;16(8):78–88.

    Google Scholar 

  34. Cai Z, et al. An intelligent Parkinson’s disease diagnostic system based on a chaotic bacterial foraging optimization enhanced fuzzy KNN approach. Comput Math Methods Med. 2018;2018:1–24.

    Article  Google Scholar 

  35. Hashem EM, Mabrouk MS. A study of support vector machine algorithm for liver disease diagnosis. Am J Intell Syst. 2014;4(1):9–14.

    Google Scholar 

  36. Vanisree K, Singaraju J. Decision support system for congenital heart disease diagnosis based on signs and symptoms using neural networks. Int J Comput Appl. 2011;19(6):6–12.

    Google Scholar 

  37. Feshki MG, Shijani OS. Improving the heart disease diagnosis by evolutionary algorithm of PSO and feed forward neural network. In: 2016 artificial intelligence and robotics (IRANOPEN). 2016. IEEE.

  38. Yan H, et al. A multilayer perceptron-based medical decision support system for heart disease diagnosis. Expert Syst Appl. 2006;30(2):272–81.

    Article  MathSciNet  Google Scholar 

  39. Mishra S, et al. EAGA-MLP—an enhanced and adaptive hybrid classification model for diabetes diagnosis. Sensors. 2020;20(14):4036.

    Article  Google Scholar 

  40. Li L-N, et al. A computer aided diagnosis system for thyroid disease using extreme learning machine. J Med Syst. 2012;36:3327–37.

    Article  Google Scholar 

  41. Chen H-L, et al. An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson’ s disease. Neurocomputing. 2016;184:131–44.

    Article  Google Scholar 

  42. Gao S. Gray level co-occurrence matrix and extreme learning machine for Alzheimer’s disease diagnosis. Int J Cogn Comput Eng. 2021;2:116–29.

    Google Scholar 

  43. Huang G-B, Ding X, Zhou H. Optimization method based extreme learning machine for classification. Neurocomputing. 2010;74(1–3):155–63.

    Article  Google Scholar 

  44. Li H-T, et al. Robust and lightweight ensemble extreme learning machine engine based on eigenspace domain for compressed learning. IEEE Trans Circuits Syst I Regul Pap. 2019;66(12):4699–712.

    Article  Google Scholar 

  45. Rong H-J, et al. A fast pruned-extreme learning machine for classification problem. Neurocomputing. 2008;72(1–3):359–66.

    Article  Google Scholar 

  46. Huang G-B, Chen L, Siew CK. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw. 2006;17(4):879–92.

    Article  Google Scholar 

  47. Lan Y, Soh YC, Huang G-B. Two-stage extreme learning machine for regression. Neurocomputing. 2010;73(16–18):3028–38.

    Article  Google Scholar 

  48. Alshayeji MH, Sindhu SC. Two-stage framework for diabetic retinopathy diagnosis and disease stage screening with ensemble learning. Expert Syst Appl. 2023;225: 120206.

    Article  Google Scholar 

  49. Liang N-Y, et al. A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw. 2006;17(6):1411–23.

    Article  Google Scholar 

  50. Chen HC, Zhang Y, Chen M. Transformer dissolved gas analysis for highly-imbalanced dataset using multi-class sequential ensembled ELM. IEEE Trans Dielectr Electr Insul. 2023. https://doi.org/10.1109/TDEI.2023.3280436.

    Article  Google Scholar 

  51. Das P, Nanda S. Bio-inspired voting ensemble weighted extreme learning machine classifier for the detection of Parkinson’s disease. Res Biomed Eng. 2023;39:1–15.

    Article  Google Scholar 

  52. Cao J, et al. Voting based extreme learning machine. Inf Sci. 2012;185(1):66–77.

    Article  MathSciNet  Google Scholar 

  53. Liu X, Li P, Gao C. Symmetric extreme learning machine. Neural Comput Appl. 2013;22:551–8.

    Article  Google Scholar 

  54. Yang S, et al. Output layer structure optimization for weighted regularized extreme learning machine based on binary method. Symmetry. 2023;15(1):244. https://doi.org/10.3390/sym15010244

    Article  Google Scholar 

  55. Hu Y, et al. Non-destructive detection of different pesticide residues on the surface of hami melon classification based on tHBA-ELM algorithm and SWIR hyperspectral imaging. Foods. 2023;12(9):1773.

    Article  Google Scholar 

  56. Mandave DD, Patil LV. Bio-inspired computing algorithms in dementia diagnosis–a application-oriented review. Results Control Optim. 2023;12: 100276.

    Article  Google Scholar 

  57. Kalaiselvi K, David VK. Enhanced extreme learning machine algorithm with deterministic weight modification for investment decision on indian stocks. In: 2022 3rd international conference on smart electronics and communication (ICOSEC). 2022. IEEE.

  58. Kalaiselvi K, David VK. Deterministic weight modification-based extreme learning machine for stock price prediction. ENG. 2023. https://doi.org/10.2174/0118722121268858231111180830.

    Article  Google Scholar 

  59. Niu B, Wang H. Bacterial colony optimization. Discret Dyn Nat Soc. 2012;2012:1–28.

    MathSciNet  Google Scholar 

  60. Arunadevi M, Sathya V. DDoS attack detection using back propagation neural network optimized by bacterial colony optimization. Int J Intell Eng Syst. 2023;16(5):301–12.

    Google Scholar 

  61. Hasan MM, et al. TaLU: a hybrid activation function combining Tanh and rectified linear unit to enhance neural networks. arXiv preprint arXiv:2305.04402, 2023.

  62. Shibata K, Ikeda Y. Effect of number of hidden neurons on learning in large-scale layered neural networks. In: 2009 ICCAS-SICE. 2009. IEEE.

  63. Dhamodharavadhani S, Rathipriya R. COVID-19 mortality rate prediction for India using statistical neural networks and gaussian process regression model. Afr Health Sci. 2021;21(1):194–206.

    Article  Google Scholar 

  64. KaleeswaranV, Dhamodharavadhani S, Rathipriya R. A comparative study of activation functions and training algorithm of NAR neural network for crop prediction. In: 2020 4th international conference on electronics, communication and aerospace technology (ICECA). 2020. IEEE

  65. Narmadha N, Rathipriya R. An optimized three-dimensional clustering for microarray data. In: Handbook of research on big data clustering and machine learning. IGI Global; 2020. p. 366–77.

    Google Scholar 

  66. Alharbi A, Alghahtani M. Using genetic algorithm and ELM neural networks for feature extraction and classification of type 2-diabetes mellitus. Appl Artif Intell. 2019;33(4):311–28.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Data Science, Shanmuga Industries Arts & Science College, Tiruvannamalai, Tamilnadu, India

    P. Vigneshvaran

  2. Department of Computer Applications, Sri Meenakshi Government College for Women, Madurai, India

    A. Vijaya Kathiravan

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  1. P. Vigneshvaran
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  2. A. Vijaya Kathiravan
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All authors contributed to the algorithms, development, and article. The final manuscript has been read and approved by all authors.

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Correspondence to P. Vigneshvaran.

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Vigneshvaran, P., Kathiravan, A.V. Optimized Extreme Learning Machine with Bacterial Colony Optimization Algorithm for Disease Diagnosis in Clinical Datasets. SN COMPUT. SCI. 5, 584 (2024). https://doi.org/10.1007/s42979-024-02864-8

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