ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Sentiment Analysis in Melanoma Cancer Detection Using Ensemble Learning Model


Affiliations
1 Department of Computer Science, St. Josephs College, Tiruchirappalli, India
     

   Subscribe/Renew Journal


Machine learning has the potential to improve healthcare by allowing clinicians to spend more time caring for patients and less time diagnosing them. This would allow clinicians to spend more time improving patient quality of life. Consequently, it is able to compute the risk of melanoma on a patient level and advise users to schedule a medical checkup rather than evaluating whether or not a specific lesion image that is provided by a patient is malignant. This is because the result of this is that it is able to compute the risk of melanoma at the patient level. By doing so, both the credibility and legislation issues are resolved, and the application is transformed into one that is adaptable. In this paper, we develop a machine learning ensemble to classify the melanoma cancer. The simulation is conducted in terms of training, testing accuracy, precision and recall. The results show that the proposed method achieves higher classification rate than other methods.

Keywords

Machine Learning, Ensemble, Prediction, Melanoma
Subscription Login to verify subscription
User
Notifications
Font Size

  • V.R. Allugunti, “A Machine Learning Model for Skin Disease Classification using Convolution Neural Network”, International Journal of Computing, Programming and Database Management, Vol. 3, No. 1, pp. 141-147, 2022.

  • Leilei Sun, Chuanren Liu, Chonghui Guo, Hui Xiong and Yanming Xie, “Data-driven Automatic Treatment Regimen Development and Recommendation”, Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 1865-1874, 2016.

  • R. Manikandan and M. Ramkumar, “Sequential Pattern Mining on Chemical Bonding Database in the Bioinformatics Field”, Proceedings of International Conference on AIP, Vol. 2393, No. 1, pp. 1-13, 2022.

  • C. Alvino Rock and R.J.S. Jeba Kumar, “Computer Aided Skin Disease (CASD) Classification using Machine Learning Techniques for iOS Platform”, Tracking and Preventing Diseases with Artificial Intelligence, pp. 201-216, 2022.

  • B.R. Nanditha, “Oral Cancer Detection using Machine Learning and Deep Learning Techniques”, International Journal of Current Research and Review, Vol. 14, No. 1, pp. 64-78, 2022.

  • N. Sultana, “Predicting Sun Protection measures against Skin Diseases using Machine Learning Approaches”, Journal of Cosmetic Dermatology, Vol. 21, No. 2, pp. 758-769, 2022.

  • Y.N. Chen and W.B. Wei, “Machine Learning Models for Outcome Prediction of Chinese Uveal Melanoma Patients: A 15‐Year Follow‐Up Study”, Cancer Communications, Vol. 42, No. 3, pp. 273-276, 2022.

  • A.R. Khan, “Facial Emotion Recognition using Conventional Machine Learning and Deep Learning Methods: Current Achievements, Analysis and Remaining Challenges”, Information, Vol. 13, No. 6, pp. 268-278, 2022.

  • M. Pinto, A. Ammendolia and A. De Sire, “Quality of Life Predictors in Patients with Melanoma: A Machine Learning Approach”, Frontiers in Oncology, Vol. 12, pp. 843611-843618, 2022.

  • N. Sengupta and U. Ghone, “Scarcity of Publicly Available Oral Cancer Image Datasets for Machine Learning Research”, Oral Oncology, Vol. 126, pp. 105737-105743, 2022.


Abstract Views: 99

PDF Views: 2




  • Sentiment Analysis in Melanoma Cancer Detection Using Ensemble Learning Model

Abstract Views: 99  |  PDF Views: 2

Authors

M. Uma Maheswari
Department of Computer Science, St. Josephs College, Tiruchirappalli, India
A. Aloysius
Department of Computer Science, St. Josephs College, Tiruchirappalli, India

Abstract


Machine learning has the potential to improve healthcare by allowing clinicians to spend more time caring for patients and less time diagnosing them. This would allow clinicians to spend more time improving patient quality of life. Consequently, it is able to compute the risk of melanoma on a patient level and advise users to schedule a medical checkup rather than evaluating whether or not a specific lesion image that is provided by a patient is malignant. This is because the result of this is that it is able to compute the risk of melanoma at the patient level. By doing so, both the credibility and legislation issues are resolved, and the application is transformed into one that is adaptable. In this paper, we develop a machine learning ensemble to classify the melanoma cancer. The simulation is conducted in terms of training, testing accuracy, precision and recall. The results show that the proposed method achieves higher classification rate than other methods.

Keywords


Machine Learning, Ensemble, Prediction, Melanoma

References