Framework Of Machine Learning Techniques For Cancer Prediction

The goal of this work is to design a machine-learning model that accurately predicts skin, lung, and skin cancer. Cancer-related clinical, demographic, and lifestyle factors were included in the dataset used to train the model. The model was educated with the help of supervised learning methods like SVMs and ANNs. Accuracy, sensitivity, specificity, and area under the curve were only a few of the criteria used to assess the model's efficacy. The results demonstrate that using demographic and clinical data, the model can effectively estimate individuals' cancer risk. Patient outcomes and healthcare costs may both benefit from this project's efforts to diagnose and treat cancer earlier. With AI and ML research at the forefront of academia, many new uses have been found for these technologies in recent years. It's not just some abstract concept out there; it's relevant to our daily lives, too. As this development continues, we see a closer integration of AI into medical practice. Its central premise also considerably reduced the current imbalance in medical distribution and the demand for available resources