ICTACT Journal on Soft Computing

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Sentiment Analysis in Melanoma Cancer Detection Using Ensemble Learning Model


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

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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
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Abstract Views: 139

PDF Views: 2




  • Sentiment Analysis in Melanoma Cancer Detection Using Ensemble Learning Model

Abstract Views: 139  |  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