Articles
| Open Access |
DOI: Predicting the Effectiveness of Laser Therapy in Periodontal Diseases Using Machine Learning Models
Han Thi Ngoc Phan , Dentist, Pham Hung Dental Center MTV Company Limited, Pham Hung Street, Binh Chanh district, Ho Chi Minh city, Vietnam Arjina Akter , Department Of Public Health, Central Michigan University, Mount Pleasant, Michigan, USAAbstract
This study evaluates the effectiveness of machine learning models in predicting the outcomes of laser therapy for periodontal diseases. Various algorithms, including Neural Networks, Gradient Boosting, Random Forest, and Support Vector Machine, were applied to a dataset containing clinical variables such as pocket depth and gingival inflammation. The Neural Network model achieved the highest predictive accuracy with an AUC-ROC score of 0.91, followed by Gradient Boosting at 0.90. These models outperformed traditional techniques, demonstrating that machine learning can accurately predict treatment success. The findings suggest that machine learning can aid clinicians in personalizing laser therapy, optimizing treatment, and improving patient outcomes. Further research with diverse datasets is recommended to refine these models.
Keywords
Machine learning, laser therapy, periodontal diseases, predictive accuracy
References
Md Habibur Rahman, Ashim Chandra Das, Md Shujan Shak, Md Kafil Uddin, Md Imdadul Alam, Nafis Anjum, Md Nad Vi Al Bony, & Murshida Alam. (2024). TRANSFORMING CUSTOMER RETENTION IN FINTECH INDUSTRY THROUGH PREDICTIVE ANALYTICS AND MACHINE LEARNING. The American Journal of Engineering and Technology, 6(10), 150–163. https://doi.org/10.37547/tajet/Volume06Issue10-17
Han, S. S., Kim, M. S., Lim, W., Park, G. H., Park, I., & Chang, S. E. (2020). Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. Journal of Investigative Dermatology, 140(7), 1572-1579. https://doi.org/10.1016/j.jid.2019.12.008
Hekler, A., Utikal, J. S., Enk, A. H., Berking, C., Klode, J., Hauschild, A., ... & von Kalle, C. (2019). Superior skin cancer classification by the combination of human and artificial intelligence. European Journal of Cancer, 120, 114-121. https://doi.org/10.1016/j.ejca.2019.07.013
Nasr-Esfahani, E., Samavi, S., Karimi, N., Soroushmehr, S. M., Jafari, M. H., & Ward, K. (2018). Melanoma detection by analysis of clinical images using convolutional neural networks. Artificial Intelligence in Medicine, 87, 54-63. https://doi.org/10.1016/j.artmed.2018.03.008
Tauhedur Rahman, Md Kafil Uddin, Biswanath Bhattacharjee, Md Siam Taluckder, Sanjida Nowshin Mou, Pinky Akter, Md Shakhaowat Hossain, Md Rashel Miah, & Md Mohibur Rahman. (2024). BLOCKCHAIN APPLICATIONS IN BUSINESS OPERATIONS AND SUPPLY CHAIN MANAGEMENT BY MACHINE LEARNING. International Journal of Computer Science & Information System, 9(11), 17–30. https://doi.org/10.55640/ijcsis/Volume09Issue11-03
Md Jamil Ahmmed, Md Mohibur Rahman, Ashim Chandra Das, Pritom Das, Tamanna Pervin, Sadia Afrin, Sanjida Akter Tisha, Md Mehedi Hassan, & Nabila Rahman. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION. International Journal of Computer Science & Information System, 9(11), 31–44. https://doi.org/10.55640/ijcsis/Volume09Issue11-04
Nafis Anjum, Md Nad Vi Al Bony, Murshida Alam, Mehedi Hasan, Salma Akter, Zannatun Ferdus, Md Sayem Ul Haque, Radha Das, & Sadia Sultana. (2024). COMPARATIVE ANALYSIS OF SENTIMENT ANALYSIS MODELS ON BANKING INVESTMENT IMPACT BY MACHINE LEARNING ALGORITHM. International Journal of Computer Science & Information System, 9(11), 5–16. https://doi.org/10.55640/ijcsis/Volume09Issue11-02
Shahbazi, Z., & Byun, Y. C. (2021). Integration of blockchain, IoT and machine learning for multistage quality control and enhancing security in smart manufacturing. Sensors, 21(4), 1467.
Das, A. C., Mozumder, M. S. A., Hasan, M. A., Bhuiyan, M., Islam, M. R., Hossain, M. N., ... & Alam,
Al Mamun, A., Hossain, M. S., Rishad, S. S. I., Rahman, M. M., Shakil, F., Choudhury, M. Z. M. E., ... & Sultana, S. (2024). MACHINE LEARNING FOR STOCK MARKET SECURITY MEASUREMENT: A COMPARATIVE ANALYSIS OF SUPERVISED, UNSUPERVISED, AND DEEP LEARNING MODELS. The American Journal of Engineering and Technology, 6(11), 63-76.
Haque, M. S., Amin, M. S., Ahmad, S., Sayed, M. A., Raihan, A., & Hossain, M. A. (2023, September). Predicting Kidney Failure using an Ensemble Machine Learning Model: A Comparative Study. In 2023 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) (pp. 31-37). IEEE.
Modak, C., Shahriyar, M. A., Taluckder, M. S., Haque, M. S., & Sayed, M. A. (2023, August). A Study of Lung Cancer Prediction Using Machine Learning Algorithms. In 2023 3rd International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS) (pp. 213-217). IEEE.
Modak, C., Shahriyar, M. A., Taluckder, M. S., Haque, M. S., & Sayed, M. A. (2023, August). A Study of Lung Cancer Prediction Using Machine Learning Algorithms. In 2023 3rd International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS) (pp. 213-217). IEEE.
Nguyen, T. N., Khan, M. M., Hossain, M. Z., Sharif, K. S., Das, R., & Haque, M. S. (2024). Product Demand Forecasting For Inventory Management with Freight Transportation Services Index Using Advanced Neural Networks Algorithm. American Journal of Computing and Engineering, 7(4), 50-58.
Miah, J., Khan, R. H., Ahmed, S., & Mahmud, M. I. (2023, June). A comparative study of detecting covid 19 by using chest X-ray images–A deep learning approach. In 2023 IEEE World AI IoT Congress (AIIoT) (pp. 0311-0316). IEEE.
Khan, R. H., Miah, J., Nipun, S. A. A., & Islam, M. (2023, March). A Comparative Study of Machine Learning classifiers to analyze the Precision of Myocardial Infarction prediction. In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0949-0954). IEEE.
Kayyum, S., Miah, J., Shadaab, A., Islam, M. M., Islam, M., Nipun, S. A. A., ... & Al Faisal, F. (2020, January). Data analysis on myocardial infarction with the help of machine learning algorithms considering distinctive or non-distinctive features. In 2020 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-7). IEEE.
Islam, M. M., Nipun, S. A. A., Islam, M., Rahat, M. A. R., Miah, J., Kayyum, S., ... & Al Faisal, F. (2020). An empirical study to predict myocardial infarction using k-means and hierarchical clustering. In Machine Learning, Image Processing, Network Security and Data Sciences: Second International Conference, MIND 2020, Silchar, India, July 30-31, 2020, Proceedings, Part II 2 (pp. 120-130). Springer Singapore.
Miah, J., Ca, D. M., Sayed, M. A., Lipu, E. R., Mahmud, F., & Arafat, S. Y. (2023, November). Improving Cardiovascular Disease Prediction Through Comparative Analysis of Machine Learning Models: A Case Study on Myocardial Infarction. In 2023 15th International Conference on Innovations in Information Technology (IIT) (pp. 49-54). IEEE.
Khan, R. H., Miah, J., Rahat, M. A. R., Ahmed, A. H., Shahriyar, M. A., & Lipu, E. R. (2023, September). A Comparative Analysis of Machine Learning Approaches for Chronic Kidney Disease Detection. In 2023 8th International Conference on Electrical, Electronics and Information Engineering (ICEEIE) (pp. 1-6). IEEE.
Miah, J., Cao, D. M., Sayed, M. A., Taluckder, M. S., Haque, M. S., & Mahmud, F. (2023). Advancing Brain Tumor Detection: A Thorough Investigation of CNNs, Clustering, and SoftMax Classification in the Analysis of MRI Images. arXiv preprint arXiv:2310.17720.
Rahman, M. M., Islam, A. M., Miah, J., Ahmad, S., & Mamun, M. (2023, June). sleepWell: Stress Level Prediction Through Sleep Data. Are You Stressed?. In 2023 IEEE World AI IoT Congress (AIIoT) (pp. 0229-0235). IEEE.
Rahman, M. M., Islam, A. M., Miah, J., Ahmad, S., & Hasan, M. M. (2023, June). Empirical Analysis with Component Decomposition Methods for Cervical Cancer Risk Assessment. In 2023 IEEE World AI IoT Congress (AIIoT) (pp. 0513-0519). IEEE.
Khan, R. H., Miah, J., Nipun, S. A. A., Islam, M., Amin, M. S., & Taluckder, M. S. (2023, September). Enhancing Lung Cancer Diagnosis with Machine Learning Methods and Systematic Review Synthesis. In 2023 8th International Conference on Electrical, Electronics and Information Engineering (ICEEIE) (pp. 1-5). IEEE.
Miah, J. (2024). HOW FAMILY DNA CAN CAUSE LUNG CANCER USING MACHINE LEARNING. International Journal of Medical Science and Public Health Research, 5(12), 8-14.
Miah, J., Khan, R. H., Linkon, A. A., Bhuiyan, M. S., Jewel, R. M., Ayon, E. H., ... & Tanvir Islam, M. (2024). Developing a Deep Learning Methodology to Anticipate the Onset of Diabetic Retinopathy at an Early Stage. In Innovative and Intelligent Digital Technologies; Towards an Increased Efficiency: Volume 1 (pp. 77-91). Cham: Springer Nature Switzerland.
M. I. (2024). MACHINE LEARNING APPROACHES FOR DEMAND FORECASTING: THE IMPACT OF CUSTOMER SATISFACTION ON PREDICTION ACCURACY. The American Journal of Engineering and Technology, 6(10), 42-53.
Ontor, M. R. H., Iqbal, A., Ahmed, E., & Rahman, A. LEVERAGING DIGITAL TRANSFORMATION AND SOCIAL MEDIA ANALYTICS FOR OPTIMIZING US FASHION BRANDS'PERFORMANCE: A MACHINE LEARNING APPROACH. SYSTEM (eISSN: 2536-7919 pISSN: 2536-7900), 9(11), 45-56.
Hossain, M. N., Hossain, S., Nath, A., Nath, P. C., Ayub, M. I., Hassan, M. M., ... & Rasel, M. (2024). ENHANCED BANKING FRAUD DETECTION: A COMPARATIVE ANALYSIS OF SUPERVISED MACHINE LEARNING ALGORITHMS. American Research Index Library, 23-35.
Shak, M. S., Mozumder, M. S. A., Hasan, M. A., Das, A. C., Miah, M. R., Akter, S., & Hossain, M. N. (2024). OPTIMIZING RETAIL DEMAND FORECASTING: A PERFORMANCE EVALUATION OF MACHINE LEARNING MODELS INCLUDING LSTM AND GRADIENT BOOSTING. The American Journal of Engineering and Technology, 6(09), 67-80.
Das, A. C., Mozumder, M. S. A., Hasan, M. A., Bhuiyan, M., Islam, M. R., Hossain, M. N., ... & Alam, M. I. (2024). MACHINE LEARNING APPROACHES FOR DEMAND FORECASTING: THE IMPACT OF CUSTOMER SATISFACTION ON PREDICTION ACCURACY. The American Journal of Engineering and Technology, 6(10), 42-53.
Hossain, M. N., Anjum, N., Alam, M., Rahman, M. H., Taluckder, M. S., Al Bony, M. N. V., ... & Jui, A. H. (2024). PERFORMANCE OF MACHINE LEARNING ALGORITHMS FOR LUNG CANCER PREDICTION: A COMPARATIVE STUDY. International Journal of Medical Science and Public Health Research, 5(11), 41-55.
Rahman, A., Iqbal, A., Ahmed, E., & Ontor, M. R. H. (2024). PRIVACY-PRESERVING MACHINE LEARNING: TECHNIQUES, CHALLENGES, AND FUTURE DIRECTIONS IN SAFEGUARDING PERSONAL DATA MANAGEMENT. International journal of business and management sciences, 4(12), 18-32.
Md Jamil Ahmmed, Md Mohibur Rahman, Ashim Chandra Das, Pritom Das, Tamanna Pervin, Sadia Afrin, Sanjida Akter Tisha, Md Mehedi Hassan, & Nabila Rahman. (2024). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR BANKING FRAUD DETECTION: A STUDY ON PERFORMANCE, PRECISION, AND REAL-TIME APPLICATION. International Journal of Computer Science & Information System, 9(11), 31–44. https://doi.org/10.55640/ijcsis/Volume09Issue11-04
Arif, M., Ahmed, M. P., Al Mamun, A., Uddin, M. K., Mahmud, F., Rahman, T., ... & Helal, M. (2024). DYNAMIC PRICING IN FINANCIAL TECHNOLOGY: EVALUATING MACHINE LEARNING SOLUTIONS FOR MARKET ADAPTABILITY. International Interdisciplinary Business Economics Advancement Journal, 5(10), 13-27.
Uddin, M. K., Akter, S., Das, P., Anjum, N., Akter, S., Alam, M., ... & Pervin, T. (2024). MACHINE LEARNING-BASED EARLY DETECTION OF KIDNEY DISEASE: A COMPARATIVE STUDY OF PREDICTION MODELS AND PERFORMANCE EVALUATION. International Journal of Medical Science and Public Health Research, 5(12), 58-75.
Das, A. C., Rishad, S. S. I., Akter, P., Tisha, S. A., Afrin, S., Shakil, F., ... & Rahman, M. M. (2024). ENHANCING BLOCKCHAIN SECURITY WITH MACHINE LEARNING: A COMPREHENSIVE STUDY OF ALGORITHMS AND APPLICATIONS. The American Journal of Engineering and Technology, 6(12), 150-162.
Shak, M. S., Uddin, A., Rahman, M. H., Anjum, N., Al Bony, M. N. V., Alam, M., ... & Pervin, T. (2024). INNOVATIVE MACHINE LEARNING APPROACHES TO FOSTER FINANCIAL INCLUSION IN MICROFINANCE. International Interdisciplinary Business Economics Advancement Journal, 5(11), 6-20.
Bhattacharjee, B., Mou, S. N., Hossain, M. S., Rahman, M. K., Hassan, M. M., Rahman, N., ... & Haque, M. S. U. (2024). MACHINE LEARNING FOR COST ESTIMATION AND FORECASTING IN BANKING: A COMPARATIVE ANALYSIS OF ALGORITHMS. Frontline Marketing, Management and Economics Journal, 4(12), 66-83.
Rahman, A., Iqbal, A., Ahmed, E., & Ontor, M. R. H. (2024). PRIVACY-PRESERVING MACHINE LEARNING: TECHNIQUES, CHALLENGES, AND FUTURE DIRECTIONS IN SAFEGUARDING PERSONAL DATA MANAGEMENT. Frontline Marketing, Management and Economics Journal, 4(12), 84-106.
Rahman, M. M., Akhi, S. S., Hossain, S., Ayub, M. I., Siddique, M. T., Nath, A., ... & Hassan, M. M. (2024). EVALUATING MACHINE LEARNING MODELS FOR OPTIMAL CUSTOMER SEGMENTATION IN BANKING: A COMPARATIVE STUDY. The American Journal of Engineering and Technology, 6(12), 68-83.
Das, P., Pervin, T., Bhattacharjee, B., Karim, M. R., Sultana, N., Khan, M. S., ... & Kamruzzaman, F. N. U. (2024). OPTIMIZING REAL-TIME DYNAMIC PRICING STRATEGIES IN RETAIL AND E-COMMERCE USING MACHINE LEARNING MODELS. The American Journal of Engineering and Technology, 6(12), 163-177.
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