Bio-Inspired Feature Selection for Improving AI-based Kidney Disease Prediction
DOI:
https://doi.org/10.52783/jns.v14.3922Keywords:
Kidney Disease, Particle Swarm Optimization, Feature Selection, Ultrasound Images, Artificial Intelligence, AccuracyAbstract
Kidney disease occurs when the kidneys become weakened and lose their ability to cleanse the blood. Most individuals show no symptoms in the early stages of kidney disease. As the condition progresses, toxins can accumulate in the bloodstream, causing complications such as anemia, hypertension, diabetes, osteopenia, and nerve damage. While these issues often develop gradually and without noticeable symptoms, they can eventually lead to sudden renal failure. Early identification of kidney disease allows for the most effective treatment. Predicting kidney function and disease using kidney ultrasound imaging is widely considered in clinical practice due to its safety, simplicity, and affordability. Several works on kidney disease prediction have already been done, but accuracy improvement is still needed. To solve this issue, the research proposes optimized Feature Selection (FS) and an Artificial Intelligence (AI) model for effective kidney disease prediction from ultrasound images. The own dataset with normal and diseased kidney images is created and processed. The processed image features are extracted using the Gray-Level Co-Occurrence Matrix (GLCM) technique. The most significant features are retrieved using Particle Swarm Optimization (PSO), a bio-inspired algorithm. The features of GLCM and PSO are given to the Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models for the classification of diseased and normal images. The SVM model with GLCM and PSO features and CNN with GLCM and PSO features are evaluated using accuracy, precision, recall, and F1 score. Both models show better accuracy improvement by using PSO features. The experimental findings show that the PSO-CNN model gives the maximum accuracy of 98.57% when compared with other models
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Tanner, George A. "Kidney function." Medical Physiology-Principles for Clinical Medicine. 3rd ed. Philadelphia: Lippincott Willams and Wilkins (2009): 391-418.
Ali, Ali Adeeb Hussein. "Overview of the vital roles of macro minerals in the human body." Journal of Trace Elements and Minerals 4 (2023): 100076.
Bonner, Ryan, Oltjon Albajrami, James Hudspeth, and Ashish Upadhyay. "Diabetic kidney disease." Primary Care: Clinics in Office Practice 47, no. 4 (2020): 645-659.
Yan, Ming-Tso, Chia-Ter Chao, and Shih-Hua Lin. "Chronic kidney disease: strategies to retard progression." International journal of molecular sciences 22, no. 18 (2021): 10084.
Alnazer, Israa, Pascal Bourdon, Thierry Urruty, Omar Falou, Mohamad Khalil, Ahmad Shahin, and Christine Fernandez-Maloigne. "Recent advances in medical image processing for the evaluation of chronic kidney disease." Medical Image Analysis 69 (2021): 101960.
Alkurdy, Noor Hamzah, Hadeel K. Aljobouri, and Zainab Kassim Wadi. "Ultrasound renal stone diagnosis based on convolutional neural network and vgg16 features." Int J Electr Comput Eng 13, no. 3 (2023): 3440-8.
George, Mino, and H. B. Anita. "Kidney Abnormalities Detection and Classification Using CNN-based Feature Extraction." In 2022 4th International Conference on Circuits, Control, Communication and Computing (I4C), pp. 359-362. IEEE, 2022.
Mittal, Harshit. "Kidney CT image analysis using CNN." Computer Science & InformationTechnology (CS & IT) ISSN (2023): 2231-5403.
Chen, Gongping, Jingjing Yin, Yu Dai, Jianxun Zhang, Xiaotao Yin, and Liang Cui. "A novel convolutional neural network for kidney ultrasound images segmentation." Computer methods and programs in biomedicine 218 (2022): 106712.
Chaki, Jyotismita, and Aysegul Ucar. "An efficient and robust approach using inductive transfer-based ensemble deep neural networks for kidney stone detection." IEEE Access (2024).
Nallarasan, V., Vijayakumar Ponnusamy, R. Lakshminarayanan, S. Vigneshwari, and R. Vinoth. "Prediction of Kidney Disease Utilizing a Hybrid Deep Learning Methodology." In 2024 2nd International Conference on Computer, Communication and Control (IC4), pp. 1-8. IEEE, 2024.
Saif, Dina, Amany M. Sarhan, and Nada M. Elshennawy. "Early prediction of chronic kidney disease based on ensemble of deep learning models and optimizers." Journal of electrical systems and information technology 11, no. 1 (2024): 17.
Preethi, I., K. Dharmarajan, Bhisham Sharma, Subrata Chowdhury, and Imed Ben Dhaou. "A Novel Method to Predict Chronic Kidney Disease using Optimized Deep Learning Algorithm." In 2024 21st Learning and Technology Conference (L&T), pp. 313-318. IEEE, 2024.
Rahman, Md Mustafizur, Md Al-Amin, and Jahangir Hossain. "Machine learning models for chronic kidney disease diagnosis and prediction." Biomedical Signal Processing and Control 87 (2024): 105368.
Talebi, Hossein, and Peyman Milanfar. "Learning to resize images for computer vision tasks." In Proceedings of the IEEE/CVF international conference on computer vision, pp. 497-506. 2021.
Kanan, Christopher, and Garrison W. Cottrell. "Color-to-grayscale: does the method matter in image recognition?." PloS one 7, no. 1 (2012): e29740.
Moreno-Barea, Francisco J., José M. Jerez, and Leonardo Franco. "Improving classification accuracy using data augmentation on small data sets." Expert Systems with Applications 161 (2020): 113696.
Chen, Rung-Ching, Christine Dewi, Su-Wen Huang, and Rezzy Eko Caraka. "Selecting critical features for data classification based on machine learning methods." Journal of Big Data 7, no. 1 (2020): 52.
Dhal, Pradip, and Chandrashekhar Azad. "A comprehensive survey on feature selection in the various fields of machine learning." Applied Intelligence 52, no. 4 (2022): 4543-4581.
Mohanaiah, P., P. Sathyanarayana, and L. GuruKumar. "Image texture feature extraction using GLCM approach." International journal of scientific and research publications 3, no. 5 (2013): 1-5.
Haralick, Robert M., Karthikeyan Shanmugam, and Its' Hak Dinstein. "Textural features for image classification." IEEE Transactions on systems, man, and cybernetics 6 (1973): 610-621.
Zubair, Abdul Rasak, and Oluwaseun Adewunmi Alo. "Grey level co-occurrence matrix (GLCM) based second order statistics for image texture analysis." arXiv preprint arXiv:2403.04038 (2024).
Özkan, Kürşad, Ahmet Mert, and Serkan Özdemir. "A new proposed GLCM texture feature: modified Rényi Deng entropy." The Journal of Supercomputing 79, no. 18 (2023): 21507-21527.
Eberhart, Russell, and James Kennedy. "A new optimizer using particle swarm theory." In MHS'95. Proceedings of the sixth international symposium on micro machine and human science, pp. 39-43. Ieee, 1995.
Freitas, Diogo, Luiz Guerreiro Lopes, and Fernando Morgado-Dias. "Particle swarm optimisation: a historical review up to the current developments." Entropy 22, no. 3 (2020): 362.
Houssein, Essam H., Ahmed G. Gad, Kashif Hussain, and Ponnuthurai Nagaratnam Suganthan. "Major advances in particle swarm optimization: theory, analysis, and application." Swarm and Evolutionary Computation 63 (2021): 100868.
Martí, Luis, Jesús García, Antonio Berlanga, and José M. Molina. "A stopping criterion for multi-objective optimization evolutionary algorithms." Information Sciences 367 (2016): 700-718.
Tzotsos, Angelos, and Demetre Argialas. "Support vector machine classification for object-based image analysis." Object-based image analysis: Spatial concepts for knowledge-driven remote sensing applications (2008): 663-677.
Xia, Shu-yin, Zhong-yang Xiong, Yue-guo Luo, and Li-mei Dong. "A method to improve support vector machine based on distance to hyperplane." Optik 126, no. 20 (2015): 2405-2410.
Chandra, Mayank Arya, and S. S. Bedi. "Survey on SVM and their application in image classification." International Journal of Information Technology 13, no. 5 (2021): 1-11.
Elngar, Ahmed A., Mohamed Arafa, Amar Fathy, Basma Moustafa, Omar Mahmoud, Mohamed Shaban, and Nehal Fawzy. "Image classification based on CNN: a survey." Journal of Cybersecurity and Information Management 6, no. 1 (2021): 18-50.
Shyam, Radhey. "Convolutional neural network and its architectures." Journal of Computer Technology & Applications 12, no. 2 (2021): 6-14p.
Kuo, Chien-Hao, Yang-Ho Chou, and Pao-Chi Chang. "Using deep convolutional neural networks for image retrieval." Electronic Imaging 28 (2016): 1-6.
Zhao, Qi, Shuchang Lyu, Boxue Zhang, and Wenquan Feng. "Multiactivation pooling method in convolutional neural networks for image recognition." Wireless Communications and Mobile Computing 2018, no. 1 (2018): 8196906.
Zafar, Afia, Muhammad Aamir, Nazri Mohd Nawi, Ali Arshad, Saman Riaz, Abdulrahman Alruban, Ashit Kumar Dutta, and Sultan Almotairi. "A comparison of pooling methods for convolutional neural networks." Applied Sciences 12, no. 17 (2022): 8643.
Agarap, Abien Fred. "Deep learning using rectified linear units (relu)." arXiv preprint arXiv:1803.08375 (2018).
Yang, Jing, and Guanci Yang. "Modified convolutional neural network based on dropout and the stochastic gradient descent optimizer." Algorithms 11, no. 3 (2018): 28.
Yan, Ming, Junjie Chen, Xiangyu Zhang, Lin Tan, Gan Wang, and Zan Wang. "Exposing numerical bugs in deep learning via gradient back-propagation." In Proceedings of the 29th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering, pp. 627-638. 2021.
Ferrer, Luciana. "Analysis and comparison of classification metrics." arXiv preprint arXiv:2209.05355 (2022).
Yacouby, Reda, and Dustin Axman. "Probabilistic extension of precision, recall, and f1 score for more thorough evaluation of classification models." In Proceedings of the first workshop on evaluation and comparison of NLP systems, pp. 79-91. 2020.
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