Early Detection of Skin Cancer Using Predictive Modeling

Melanoma is one of the most dangerous types of skin cancer, and catching it early can make a big difference in a patient’s chances of recovery. Here, we explored how machine learning can help predict whether a skin lesion is benign or malignant using only patient information, without needing complex medical images. We used a large dataset from the International Skin Imaging Collaboration (ISIC), which included over 33,000 records with details like the patient's age, gender, where the lesion was located, and whether it was cancerous or not. The first step was to clean and prepare the data: we handled missing values, converted text-based features into numbers, and scaled everything so the machine learning model could understand it better. After splitting the data into training and testing sets, we used basic machine learning models like logistic regression and decision trees to make predictions. We then evaluated how well the models performed using tools like the confusion matrix and ROC curves, which helped us understand how accurately the model was identifying cancerous lesions. One unique thing about this study is that we relied only on patient data instead of analyzing images. This makes the approach more practical for places with limited resources or no access to advanced imaging tools. Of course, combining this method with image-based models in the future could make it even more powerful. Overall, this project shows how structured patient data, when cleaned and processed properly, can help build effective tools to support doctors in diagnosing melanoma. In the future, we hope to improve the model further using better algorithms, deeper analysis, and possibly integrating it into real-world clinical systems.