Optimizing Cardiomegaly Detection: A Random Forest Approach to Processed Chest X-ray Imagery
Abstract
This study explores the application of a Random Forest Classifier for the automated detection of Cardiomegaly from chest X-ray images, utilizing a dataset processed and derived from the NIH Chest X-ray Dataset. Given the crucial need for accurate and timely diagnosis of Cardiomegaly to inform appropriate treatment decisions, this research aims to determine the efficacy of machine learning models in augmenting diagnostic processes. Employing image pre-processing techniques such as Sobel filtering for edge detection and Hu Moments for feature extraction, the study enhances the input features for the model. The performance of the classifier was evaluated using a 5-fold cross-validation approach, yielding results with average accuracy, precision, recall, and F1-scores ranging approximately between 52% and 54%. These findings suggest a moderate level of reliability and consistency, indicating the potential utility of ensemble machine learning methods in medical imaging analysis. However, the variability in performance across different data subsets highlights the challenges and necessitates further optimization. This research contributes to the ongoing discourse on integrating machine learning into clinical settings, demonstrating the potential benefits and current limitations. Future research is recommended to expand the dataset variety, integrate advanced deep learning methodologies, and rigorously test these models in clinical environments. The findings hold significant implications for the development of automated diagnostic tools in healthcare, potentially leading to enhanced diagnostic accuracy and efficiency.
References
S. Khomsah and E. Faizal, “Effectiveness Evaluation of the RandomForest Algorithm in Classifying CancerLips Data,” … Artif. Intell. Med. …, 2023.
H. Nhat-Duc, “Comparison of histogram-based gradient boosting classification machine, random Forest, and deep convolutional neural network for pavement raveling severity classification,” Autom. Constr., vol. 148, 2023, doi: 10.1016/j.autcon.2023.104767.
L. Zhang, “Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest),” BMC Emerg. Med., vol. 22, no. 1, 2022, doi: 10.1186/s12873-022-00582-z.
Y. Zhao, “Classification of Zambian grasslands using random forest feature importance selection during the optimal phenological period,” Ecol. Indic., vol. 135, 2022, doi: 10.1016/j.ecolind.2021.108529.
R. A. Azdy, R. F. Syam, E. Faizal, and ..., “Performance Evaluation of Bagging Meta-Estimator in Lung Disease Detection: A Case Study on Imbalanced Dataset,” Int. J. …, 2023.
R. Setiawan, A. Parewe, A. J. Latipah, and ..., “Assessing Bagging-meta Estimator in Imbalanced CT Kidney Disease Classification: A Focus on Sobel and Hu Moment Techniques,” … Artif. Intell. …, 2023.
N. Rismayanti, A. Naswin, U. Zaky, M. Zakariyah, and D. A. Purnamasari, “Evaluating Thresholding-Based Segmentation and Humoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes,” Int. J. Artif. Intell. Med. Issues, vol. 1, no. 2, 2023.
Y. Jusman, “Machine Learnings of Dental Caries Images based on Hu Moment Invariants Features,” Proc. - 2021 Int. Semin. Appl. Technol. Inf. Commun. IT Oppor. Creat. Digit. Innov. Commun. within Glob. Pandemic, iSemantic 2021, pp. 296–299, 2021, doi: 10.1109/iSemantic52711.2021.9573208.
R. A. Disha, “Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique,” Cybersecurity, vol. 5, no. 1, 2022, doi: 10.1186/s42400-021-00103-8.
M. Rafało, “Cross validation methods: Analysis based on diagnostics of thyroid cancer metastasis,” ICT Express, vol. 8, no. 2, pp. 183–188, 2022, doi: 10.1016/j.icte.2021.05.001.
J. Zhang, “Multi-class object detection using faster R-CNN and estimation of shaking locations for automated shake-and-catch apple harvesting,” Comput. Electron. Agric., vol. 173, 2020, doi: 10.1016/j.compag.2020.105384.
T. A. Reist, “Cross validation of aerodynamic shape optimization methodologies for aircraft wing-body optimization,” AIAA J., vol. 58, no. 6, pp. 2581–2595, 2020, doi: 10.2514/1.J059091.
D. R. D. Varma, “Performance Monitoring of Novel Iris Detection System using Sobel Algorithm in Comparison with Canny Algorithm by Minimizing the Mean Square Error,” Proc. 3rd Int. Conf. Intell. Eng. Manag. ICIEM 2022, pp. 509–512, 2022, doi: 10.1109/ICIEM54221.2022.9853127.
C. Xiu, “Image Segmentation of CV Model Combined with Sobel Operator,” Proc. 32nd Chinese Control Decis. Conf. CCDC 2020, pp. 4356–4360, 2020, doi: 10.1109/CCDC49329.2020.9164450.
J. N. Archana, “Enhancement of digital chest images using a modified Sobel edge detection algorithm,” Indones. J. Electr. Eng. Comput. Sci., vol. 24, no. 3, pp. 1718–1726, 2021, doi: 10.11591/ijeecs.v24.i3.pp1718-1726.
X. Yu, “Random forest algorithm-based classification model of pesticide aquatic toxicity to fishes,” Aquat. Toxicol., vol. 251, 2022, doi: 10.1016/j.aquatox.2022.106265.
S. Ortiz-Toquero, “Classification of Keratoconus Based on Anterior Corneal High-order Aberrations: A Cross-validation Study,” Optom. Vis. Sci., vol. 97, no. 3, pp. 169–177, 2020, doi: 10.1097/OPX.0000000000001489.
K. M. Bain, “Cross-validation of three Advanced Clinical Solutions performance validity tests: Examining combinations of measures to maximize classification of invalid performance,” Appl. Neuropsychol., vol. 28, no. 1, pp. 24–34, 2021, doi: 10.1080/23279095.2019.1585352.
A. Fitria and H. Azis, “Analisis Kinerja Sistem Klasifikasi Skripsi menggunakan Metode Naïve Bayes Classifier,” Pros. Semin. Nas. Ilmu Komput. dan Teknol. Inf., vol. 3, no. 2, pp. 102–106, 2018.
M. M. Baharuddin, T. Hasanuddin, and H. Azis, “Analisis Performa Metode K-Nearest Neighbor untuk Identifikasi Jenis Kaca,” Ilk. J. Ilm., vol. 11, no. 28, pp. 269–274, 2019.
H. Azis, F. T. Admojo, and E. Susanti, “Analisis Perbandingan Performa Metode Klasifikasi pada Dataset Multiclass Citra Busur Panah,” Techno.Com, vol. 19, no. 3, 2020.
H. Azis, F. Fattah, and P. Putri, “Performa Klasifikasi K-NN dan Cross-validation pada Data Pasien Pengidap Penyakit Jantung,” Ilk. J. Ilm., vol. 12, no. 2, pp. 81–86, 2020.
N. A’ayunnisa, Y. Salim, and H. Azis, “Analisis performa metode Gaussian Naïve Bayes untuk klasifikasi citra tulisan tangan karakter arab,” … J. Data Sci., 2022.
A. Nurul, Y. Salim, and H. Azis, “Analisis performa metode Gaussian Naïve Bayes untuk klasifikasi citra tulisan tangan karakter arab,” Indones. J. Data Sci., vol. 3, no. 3, pp. 115–121, 2022, doi: https://doi.org/10.56705/ijodas.v3i3.54.
