Predicting Melanoma Patient Prognosis using SVM and MLP Algorithms
Abstract
Malignant melanoma is a deadly form of skin cancer with a high mortality rate. To improve patient outcomes and prognosis, accurate prediction models are essential. This research paper explores the application of Support Vector Machines (SVM) and Multilayer Perceptron (MLP) algorithms to predict the prognosis of patients with malignant melanoma based on tumor measurements. The dataset comprises 205 patients who underwent tumor removal surgery. Key measurements, such as tumor thickness and ulceration status, are analyzed as potential prognostic variables. The performance of SVM and MLP algorithms in predicting patient outcomes is assessed and compared, offering insights into the effectiveness of each approach. The findings of this study have significant implications for personalized treatment strategies and patient survival rates in melanoma management.
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Introduction
Dangerous melanoma is the most serious type of skin malignant growth, and it is likewise one of the malignant growth illnesses which have exhibited the biggest expansion in occurrence in Sweden during ongoing many years [5]. A significant part of the increment has been proposed to be made by the expansion in over the top openness daylight by the populace, since it is notable that bright radiation expands the gamble of creating melanoma. One more contributing component to the gamble of creating melanoma is the skin type, While Caucasians, freckled people, and individuals who are vulnerable to red sun related burn have the most noteworthy gamble of creating melanoma.
Conclusion
These findings have significant implications for personalized treatment strategies and patient care in melanoma management. Accurate prognostic models can aid clinicians in identifying patients at higher risk of adverse outcomes, enabling timely interventions and improved survival rates. Further research and validation on larger and diverse datasets are warranted to confirm the robustness and applicability of these models in real-world clinical settings. Overall, this study contributes valuable insights into the use of machine learning algorithms for predicting malignant melanoma outcomes and lays the groundwork for future advancements in melanoma research and patient care.