Gallbladder Disease Classification from Ultrasound Images Using CNN Feature Extraction and Machine Learning Optimization
Abstract
Gallbladder diseases, including gallstones, carcinoma, and adenomyomatosis, may cause severe complications if not identified correctly and in a timely manner. However, ultrasound image interpretation relies heavily on operator experience and may suffer from subjectivity and inconsistency. This study aims to develop an automated and optimized classification model for gallbladder disease using ultrasound images, aiming to improve diagnostic reliability and efficiency. A key outcome of this research is a thorough assessment of how feature selection combined with hyperparameter tuning influences the accuracy of classical machine learning models that use features extracted via CNN-based feature extraction. The proposed pipeline enhances diagnostic accuracy while remaining computationally efficient. The method involves extracting deep features from ultrasound images using a pre-trained VGG16 CNN model. The features are subsequently reduced using the SelectKBest method through Univariate Feature Selection. Multiple popular classification models, specifically SVM, Random Forest, KNN, and Logistic Regression were tested using both original settings and adjusted hyperparameters through grid search. A complete evaluation of model performance was conducted using the test set, employing key performance indicators including overall prediction correctness (accuracy), actual positive rate (recall), positive prediction accuracy (precision), F1-score, and the ROC curve’s corresponding area value. Evaluation results suggest that the SVM approach, combined with selected features and hyperparameter tuning, achieved the highest performance: 99.35% accuracy, 99.32% precision, 99.35% recall, and 99.33% F1-score, with a relatively short computation time of 18.4 seconds. In conclusion, feature selection and hyperparameter tuning significantly enhance classification performance, making the proposed method a promising candidate for clinical decision support in gallbladder disease diagnosis using ultrasound imaging.
Downloads
References
A. M. Lucchese, M. C. Machry, A. N. Kalil, A. F. Oliveira, and F. J. F. Coimbra, “Exploring Anatomy, Macroscopic and Microscopic Structures, and Physiological Functions of the Gallbladder,” in Gallbladder Cancer, Cham: Springer Nature Switzerland, 2024, pp. 1–13. doi: 10.1007/978-3-031-76746-3_1.
J. L. Turumin, V. A. Shanturov, and H. E. Turumina, “The role of the gallbladder in humans,” Rev Gastroenterol Mex, vol. 78, no. 3, pp. 177–187, Jul. 2013, doi: 10.1016/j.rgmx.2013.02.003.
G. Doherty, M. Manktelow, B. Skelly, P. Gillespie, A. J. Bjourson, and S. Watterson, “The Need for Standardizing Diagnosis, Treatment and Clinical Care of Cholecystitis and Biliary Colic in Gallbladder Disease,” Medicina (B Aires), vol. 58, no. 3, p. 388, Mar. 2022, doi: 10.3390/medicina58030388.
R. Lam, A. Zakko, J. C. Petrov, P. Kumar, A. J. Duffy, and T. Muniraj, “Gallbladder Disorders: A Comprehensive Review,” Disease-a-Month, vol. 67, no. 7, p. 101130, Jul. 2021, doi: 10.1016/j.disamonth.2021.101130.
N. M. Parra-Landazury, J. Cordova-Gallardo, and N. Méndez-Sánchez, “Obesity and Gallstones,” Visc Med, vol. 37, no. 5, pp. 394–402, 2021, doi: 10.1159/000515545.
X. Zhang, L. Guan, H. Tian, and Y. Li, “Prevalence and Risk Factors of Gallbladder Stones and Polyps in Liaoning, China,” Front Med (Lausanne), vol. 9, Apr. 2022, doi: 10.3389/fmed.2022.865458.
Z.-Z. Li, L.-J. Guan, R. Ouyang, Z.-X. Chen, G.-Q. Ouyang, and H.-X. Jiang, “Global, regional, and national burden of gallbladder and biliary diseases from 1990 to 2019,” World J Gastrointest Surg, vol. 15, no. 11, pp. 2564–2578, Nov. 2023, doi: 10.4240/wjgs.v15.i11.2564.
M. Piñeros et al., “Global variations in gallbladder cancer incidence: What do recorded data and national estimates tell us?,” Int J Cancer, vol. 156, no. 7, pp. 1358–1368, Apr. 2025, doi: 10.1002/ijc.35232.
W. Kratzer, M. Klysik, A. Binzberger, and J. Schmidberger, “Gallbladder stone incidence and prevalence in Germany: a population-based study,” Z Gastroenterol, vol. 59, no. 08, pp. 859–864, Aug. 2021, doi: 10.1055/a-1401-2170.
K. Z. P. Wibowo, I. B. B. S. Adnyana, S. Indrakila, and N. Agustriani, “Interrelation between body mass index and the occurrence of gallstones,” International Journal of Surgery Science, vol. 8, no. 2, pp. 46–51, Jan. 2024, doi: 10.33545/surgery.2024.v8.i2a.1083.
J.-Y. Choi and D. R. Chang, “Imaging Diagnosis of Diseases of the Gallbladder: US, CT, and MRI,” in Diseases of the Gallbladder, Singapore: Springer Singapore, 2020, pp. 45–60. doi: 10.1007/978-981-15-6010-1_4.
L. Mencarini, A. Vestito, R. M. Zagari, and M. Montagnani, “New Developments in the Ultrasonography Diagnosis of Gallbladder Diseases,” Gastroenterol Insights, vol. 15, no. 1, pp. 42–68, Jan. 2024, doi: 10.3390/gastroent15010004.
T. Najeebi, L. Aljailani, H. Falamarzi, and S. Alghanem, “Impact of emergency department ultrasound in diagnosing patients with right upper quadrant pain in a tertiary hospital in the Kingdom of Bahrain: A cross-sectional study,” Med Sci, vol. 27, no. 136, pp. 1–7, Jun. 2023, doi: 10.54905/disssi/v27i136/e251ms2949.
R. Farina and A. Sparano, “Errors in Sonography,” in Errors in Radiology, Milano: Springer Milan, 2012, pp. 79–85. doi: 10.1007/978-88-470-2339-0_8.
R. S. Sirisati, C. S. Kumar, P. Venuthurumilli, J. Ranjith, and K. S. Rao, “Cancer Sight: Illuminating the Hidden-Advancing Breast Cancer Detection with Machine Learning-Based Image Processing Techniques,” in 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), IEEE, Nov. 2023, pp. 1618–1625. doi: 10.1109/ICSCNA58489.2023.10370462.
X. Wang, H. Zhang, Z. Bai, X. Xie, and Y. Feng, “Current status of artificial intelligence analysis for the diagnosis of gallbladder diseases using ultrasonography: a scoping review,” Transl Gastroenterol Hepatol, vol. 10, pp. 12–12, Jan. 2025, doi: 10.21037/tgh-24-61.
A. M. Obaid, A. Turki, H. Bellaaj, M. Ksantini, A. AlTaee, and A. Alaerjan, “Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method,” Diagnostics, vol. 13, no. 10, p. 1744, May 2023, doi: 10.3390/diagnostics13101744.
A. Bozdag, M. Yildirim, M. Karaduman, H. B. Mutlu, G. Karaduman, and A. Aksoy, “Detection of Gallbladder Disease Types Using a Feature Engineering-Based Developed CBIR System,” Diagnostics, vol. 15, no. 5, p. 552, Feb. 2025, doi: 10.3390/diagnostics15050552.
S. B. Shuvo and M. Z. Chowdhury, “Classification of Gallbladder Cancer Using Average Ensemble Learning,” in 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), IEEE, May 2024, pp. 1450–1455. doi: 10.1109/ICEEICT62016.2024.10534480.
S. Dadjouy and H. Sajedi, “Gallbladder cancer detection via ultrasound image analysis: An end‐to‐end hierarchical feature‐fused model,” IET Image Process, vol. 19, no. 1, Jan. 2025, doi: 10.1049/ipr2.13292.
L. Li, “Deep Learning-based EEG Signal Identity Recognition Using VGGNet,” in 2024 4th International Conference on Neural Networks, Information and Communication (NNICE), IEEE, Jan. 2024, pp. 1092–1095. doi: 10.1109/NNICE61279.2024.10498553.
M. M. Bala, K. J. L. Bai, S. Kattubadi, G. Pulipati, and A. Balguri, “Deep Learning Transformations in Content-Based Image Retrieval: Exploring the Visual Geometry Group (VGG16) Model,” in 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG), IEEE, Dec. 2024, pp. 1–4. doi: 10.1109/ICTBIG64922.2024.10911292.
W. N. Waluyo, R. Rizal Isnanto, and Adian Fatchur Rochim, “Comparison of Mycobacterium Tuberculosis Image Detection Accuracy Using CNN and Combination CNN-KNN,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 1, pp. 80–87, Feb. 2023, doi: 10.29207/resti.v7i1.4626.
A. Biswas and Md. S. Islam, “A Hybrid Deep CNN-SVM Approach for Brain Tumor Classification,” Journal of Information Systems Engineering and Business Intelligence, vol. 9, no. 1, pp. 1–15, Apr. 2023, doi: 10.20473/jisebi.9.1.1-15.
A. Yahya Saleh, C. Ka Chin, and R. Ameera Rosdi, “Transfer Learning for Lung Nodules Classification with CNN and Random Forest,” Pertanika J Sci Technol, vol. 32, no. 1, pp. 463–479, Nov. 2023, doi: 10.47836/pjst.32.1.25.
B. Kuntiyellannagari, B. Dwarakanath, and P. V. Reddy, “Hybrid model for brain tumor detection using convolution neural networks,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 33, no. 3, p. 1775, Mar. 2024, doi: 10.11591/ijeecs.v33.i3.pp1775-1781.
M. S. Tahosin, M. A. Sheakh, T. Islam, R. J. Lima, and M. Begum, “Optimizing brain tumor classification through feature selection and hyperparameter tuning in machine learning models,” Inform Med Unlocked, vol. 43, p. 101414, 2023, doi: 10.1016/j.imu.2023.101414.
F. Mohammadi and A. J. Irani, “A Review of Feature Selection Methods for Disease Risk Prediction and healthcare : Review of Feature Selection Methods for Disease Prediction,” in 2024 11th International Symposium on Telecommunications (IST), IEEE, Oct. 2024, pp. 713–719. doi: 10.1109/IST64061.2024.10843597.
Q. Bani Baker and M. F. Alajlouni, “Comparative Analysis of Feature Selection Techniques with Metaheuristic Grasshopper Optimization Algorithm,” 2024, pp. 159–169. doi: 10.1007/978-3-031-56728-5_14.
F. N. Osman, M. A. Abdul Aziz, and M. N. Taib, “Comparative Evaluation of Feature Selection Algorithms for Predictive Modeling of Academic Performance Outcomes,” in 2024 IEEE 13th International Conference on Engineering Education (ICEED), IEEE, Nov. 2024, pp. 1–6. doi: 10.1109/ICEED62316.2024.10923823.
F. Rahmat et al., “Supervised feature selection using principal component analysis,” Knowl Inf Syst, vol. 66, no. 3, pp. 1955–1995, Mar. 2024, doi: 10.1007/s10115-023-01993-5.
S. Abdumalikov, J. Kim, and Y. Yoon, “Performance Analysis and Improvement of Machine Learning with Various Feature Selection Methods for EEG-Based Emotion Classification,” Applied Sciences, vol. 14, no. 22, p. 10511, Nov. 2024, doi: 10.3390/app142210511.
S. Jain and A. Saha, “Rank-based univariate feature selection methods on machine learning classifiers for code smell detection,” Evol Intell, vol. 15, no. 1, pp. 609–638, Mar. 2022, doi: 10.1007/s12065-020-00536-z.
S. Julkaew, T. Wongsirichot, K. Damkliang, and P. Sangthawan, “Improving accuracy of vascular access quality classification in hemodialysis patients using deep learning with K highest score feature selection,” Journal of International Medical Research, vol. 52, no. 4, Apr. 2024, doi: 10.1177/03000605241232519.
L. W. Rizkallah, “Optimizing SVM hyperparameters for satellite imagery classification using metaheuristic and statistical techniques,” Int J Data Sci Anal, Apr. 2025, doi: 10.1007/s41060-025-00762-7.
H. Dabool, H. Alashwal, H. Alnuaimi, A. Alhouqani, S. Alkaabi, and A. Al Ahbabi, “Comparative Analysis of Hyperparameter Tuning Methods in Classification Models For Ensemble Learning,” in 2024 7th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI), IEEE, Dec. 2024, pp. 1–5. doi: 10.1109/ACAI63924.2024.10899492.
D. M. Belete and M. D. Huchaiah, “Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results,” International Journal of Computers and Applications, vol. 44, no. 9, pp. 875–886, Sep. 2022, doi: 10.1080/1206212X.2021.1974663.
A. Taufiq, S. Yulianti, A. Rahmatulloh, I. Darmawan, and R. Rizal, “Comparison of Hyperparameter Tuning Techniques on KNN Algorithm to find the Best K Value using Grid Search and Random Search Methods,” in 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), IEEE, Dec. 2024, pp. 180–186. doi: 10.1109/ISRITI64779.2024.10963519.
Sukamto, Hadiyanto, and Kurnianingsih, “KNN Optimization Using Grid Search Algorithm for Preeclampsia Imbalance Class,” E3S Web of Conferences, vol. 448, p. 02057, Nov. 2023, doi: 10.1051/e3sconf/202344802057.
A. Turki, A. M. Obaid, H. Bellaaj, M. Ksantini, and A. AlTaee, “UIdataGB: Multi-Class ultrasound images dataset for gallbladder disease detection,” Data Brief, vol. 54, p. 110426, Jun. 2024, doi: 10.1016/j.dib.2024.110426.
S. M. Pizer et al., “Adaptive histogram equalization and its variations,” Comput Vis Graph Image Process, vol. 39, no. 3, pp. 355–368, Sep. 1987, doi: 10.1016/S0734-189X(87)80186-X.
K. Zuiderveld, “Contrast Limited Adaptive Histogram Equalization,” in Graphics Gems, Elsevier, 1994, pp. 474–485. doi: 10.1016/B978-0-12-336156-1.50061-6.
A. M. Reza, “Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement,” The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology, vol. 38, no. 1, pp. 35–44, Aug. 2004, doi: 10.1023/B:VLSI.0000028532.53893.82.
I. K. Seneng, P. D. W. Ayu, and R. R. Huizen, “Comparative Analysis of Augmentation and Filtering Methods in VGG19 and DenseNet121 for Breast Cancer Classification,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 3, pp. 1131–1146, Jun. 2025, doi: 10.52436/1.jutif.2025.6.3.4397.
N. Nafi’iyah and J. Maknun, “CNN Architecture for Classifying Types of Mango Based on Leaf Images,” Telematika, vol. 14, no. 2, pp. 112–121, Aug. 2021, doi: 10.35671/telematika.v14i2.1262.
P. Dehbozorgi, O. Ryabchykov, and T. W. Bocklitz, “A comparative study of statistical, radiomics, and deep learning feature extraction techniques for medical image classification in optical and radiological modalities,” Comput Biol Med, vol. 187, p. 109768, Mar. 2025, doi: 10.1016/j.compbiomed.2025.109768.
S. K. Dash et al., “Ocular Disease Detection Using Fundus Images: A Hybrid Approach of Grad‐CAM and Multiscale Retinex Preprocessing With VGG16 Deep Features and Fine KNN Classification,” Applied Computational Intelligence and Soft Computing, vol. 2025, no. 1, Jan. 2025, doi: 10.1155/acis/6653543.
T.-H. Nguyen, T.-N. Nguyen, and B.-V. Ngo, “A VGG-19 Model with Transfer Learning and Image Segmentation for Classification of Tomato Leaf Disease,” AgriEngineering, vol. 4, no. 4, pp. 871–887, Oct. 2022, doi: 10.3390/agriengineering4040056.
X. Cheng, “A Comprehensive Study of Feature Selection Techniques in Machine Learning Models,” Insights in Computer, Signals and Systems, vol. 1, no. 1, pp. 65–78, Nov. 2024, doi: 10.70088/xpf2b276.
M. Islam and R. Islam, “Exploring the Impact of Univariate Feature Selection Method on Machine Learning Algorithms for Heart Disease Prediction,” in 2023 International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM), IEEE, Jun. 2023, pp. 1–5. doi: 10.1109/NCIM59001.2023.10212832.
B. B. Gupta, A. Gaurav, R. W. Attar, V. Arya, A. Alhomoud, and K. T. Chui, “Sustainable IoT Security in Entrepreneurship: Leveraging Univariate Feature Selection and Deep CNN Model for Innovation and Knowledge,” Sustainability, vol. 16, no. 14, p. 6219, Jul. 2024, doi: 10.3390/su16146219.
B. Mohammad Hasani Zade, N. Mansouri, and M. M. Javidi, “An improved beluga whale optimization using ring topology for solving multi-objective task scheduling in cloud,” Comput Ind Eng, vol. 200, p. 110836, Feb. 2025, doi: 10.1016/j.cie.2024.110836.
S. Adige, R. Kurban, A. Durmuş, and E. Karaköse, “Classification of apple images using support vector machines and deep residual networks,” Neural Comput Appl, vol. 35, no. 16, pp. 12073–12087, Jun. 2023, doi: 10.1007/s00521-023-08340-3.
X. Wu, T. Oli, J. H. Qian, V. Taylor, M. C. Hersam, and V. K. Sangwan, “An Autotuning-based Optimization Framework for Mixed-kernel SVM Classifications in Smart Pixel Datasets and Heterojunction Transistors,” Sep. 2024.
L. Zhu and P. Spachos, “Support vector machine and YOLO for a mobile food grading system,” Internet of Things, vol. 13, p. 100359, Mar. 2021, doi: 10.1016/j.iot.2021.100359.
M. Hussain, S. K. Wajid, A. Elzaart, and M. Berbar, “A Comparison of SVM Kernel Functions for Breast Cancer Detection,” in 2011 Eighth International Conference Computer Graphics, Imaging and Visualization, IEEE, Aug. 2011, pp. 145–150. doi: 10.1109/CGIV.2011.31.
S. M. Al-azzawi, M. A. Deif, H. Attar, A. Amer, and A. A. A. Solyman, “Hyperparameter Optimization of Regression Model for Electrical Load Forecasting During the COVID-19 Pandemic Lockdown Period,” International Journal of Intelligent Engineering and Systems, vol. 16, no. 4, pp. 239–253, Aug. 2023, doi: 10.22266/ijies2023.0831.20.
L. Breiman, “Random Forests,” Mach Learn, vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1023/A:1010933404324.
A. Parmar, R. Katariya, and V. Patel, “A Review on Random Forest: An Ensemble Classifier,” 2019, pp. 758–763. doi: 10.1007/978-3-030-03146-6_86.
N. Z. Al Habesyah, R. Herteno, F. Indriani, I. Budiman, and D. Kartini, “Sentiment Analysis of TikTok Shop Closure in Indonesia on Twitter Using Supervised Machine Learning,” Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 6, no. 2, pp. 148–156, Apr. 2024, doi: 10.35882/jeeemi.v6i2.381.
W. Li, Y. Chen, and Y. Song, “Boosted K-nearest neighbor classifiers based on fuzzy granules,” Knowl Based Syst, vol. 195, p. 105606, May 2020, doi: 10.1016/j.knosys.2020.105606.
V. Kalra, I. Kashyap, and H. Kaur, “Effect of Distance Measures on K-Nearest Neighbour Classifier,” in 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA), IEEE, Sep. 2022, pp. 1–7. doi: 10.1109/ICCSEA54677.2022.9936314.
M. Mailagaha Kumbure and P. Luukka, “A generalized fuzzy k-nearest neighbor regression model based on Minkowski distance,” Granular Computing, vol. 7, no. 3, pp. 657–671, Jul. 2022, doi: 10.1007/s41066-021-00288-w.
T. Ciu and R. S. Oetama, “Logistic Regression Prediction Model for Cardiovascular Disease,” IJNMT (International Journal of New Media Technology), vol. 7, no. 1, pp. 33–38, Jul. 2020, doi: 10.31937/ijnmt.v7i1.1340.
R. T. Yunardi, R. Apsari, and M. Yasin, “Comparison of Machine Learning Algorithm For Urine Glucose Level Classification Using Side-Polished Fiber Sensor,” Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 2, no. 2, pp. 33–39, Jul. 2020, doi: 10.35882/jeeemi.v2i2.1.
A. Balboa, A. Cuesta, J. González-Villa, G. Ortiz, and D. Alvear, “Logistic regression vs machine learning to predict evacuation decisions in fire alarm situations,” Saf Sci, vol. 174, p. 106485, Jun. 2024, doi: 10.1016/j.ssci.2024.106485.
Putri Nabella, Rudy Herteno, Setyo Wahyu Saputro, Mohammad Reza Faisal, and Friska Abadi, “Impact of a Synthetic Data Vault for Imbalanced Class in Cross-Project Defect Prediction,” Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 6, no. 2, pp. 219–230, Apr. 2024, doi: 10.35882/jeeemi.v6i2.409.
B. H. Shekar and G. Dagnew, “Grid Search-Based Hyperparameter Tuning and Classification of Microarray Cancer Data,” in 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), IEEE, Feb. 2019, pp. 1–8. doi: 10.1109/ICACCP.2019.8882943.
T. N. Nuklianggraita, A. Adiwijaya, and A. Aditsania, “On the Feature Selection of Microarray Data for Cancer Detection based on Random Forest Classifier,” JURNAL INFOTEL, vol. 12, no. 3, pp. 89–96, Aug. 2020, doi: 10.20895/infotel.v12i3.485.
C. Tantithamthavorn, S. McIntosh, A. E. Hassan, and K. Matsumoto, “The Impact of Automated Parameter Optimization on Defect Prediction Models,” IEEE Transactions on Software Engineering, vol. 45, no. 7, pp. 683–711, Jul. 2019, doi: 10.1109/TSE.2018.2794977.
M. Jena and S. Dehuri, “An Integrated Novel Framework for Coping Missing Values Imputation and Classification,” IEEE Access, vol. 10, pp. 69373–69387, 2022, doi: 10.1109/ACCESS.2022.3187412.
Siti Napi’ah, Triando Hamonangan Saragih, Dodon Turianto Nugrahadi, Dwi Kartini, and Friska Abadi, “Implementation of Monarch Butterfly Optimization for Feature Selection in Coronary Artery Disease Classification Using Gradient Boosting Decision Tree,” Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 5, no. 4, Oct. 2023, doi: 10.35882/jeeemi.v5i4.331.
D. Chicco, V. Starovoitov, and G. Jurman, “The Benefits of the Matthews Correlation Coefficient (MCC) Over the Diagnostic Odds Ratio (DOR) in Binary Classification Assessment,” IEEE Access, vol. 9, pp. 47112–47124, 2021, doi: 10.1109/ACCESS.2021.3068614.
Luh Ayu Martini, G. A. Pradipta, and R. R. Huizen, “Analysis of the Impact of Data Oversampling on the Support Vector Machine Method for Stroke Disease Classification,” Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 7, no. 2, pp. 404–421, Mar. 2025, doi: 10.35882/jeeemi.v7i2.698.
W. Islam et al., “A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients,” Diagnostics, vol. 14, no. 9, p. 954, May 2024, doi: 10.3390/diagnostics14090954.
Shalehah, Muhammad Itqan Mazdadi, Andi Farmadi, Dwi Kartini, and Muliadi, “Implementation of Particle Swarm Optimization Feature Selection on Naïve Bayes for Thoracic Surgery Classification,” Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 5, no. 3, pp. 150–158, Jul. 2023, doi: 10.35882/jeemi.v5i3.305.
Jin Huang and C. X. Ling, “Using AUC and accuracy in evaluating learning algorithms,” IEEE Trans Knowl Data Eng, vol. 17, no. 3, pp. 299–310, Mar. 2005, doi: 10.1109/TKDE.2005.50.
Copyright (c) 2025 Ryan Adhitama Putra, Gede Angga Pradipta, Putu Desiana Wulaning Ayu
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
.png)
.png)
.png)
.png)
.png)
