Lung Cancer Prediction using Machine Learning

Past years have experienced increasing mortality rate due to lung cancer and thus it become crucial to predict whether the tumor has transformed to cancer or not, if the prediction is made at an early stage then many lives can be saved. This investigates the potential of machine learning (ML) algorithms for predicting lung cancer risk based on clinical data and Medical Imaging, patient demographics and several ML models, including Decision Trees, Support Vector Machines (SVM), random forests and K- Nearest neighbors(KNN),CNN(convolutional neural network).Evaluation metrics like accuracy ,precision, recall, were used to assess model effectiveness and based on classification results obtained. Prediction is made whether the tumor is begin or malignant. The inevitable parameters such as accuracy, recall, and precision are calculated for determining which algorithm has the highest predictive accuracy. The findings suggest that machine learning can play a pivotal role in identifying high-risk patients early, facilitating timely intervention and improving outcome. Key Words: Support Vector Machines (SVM), k-Nearest Neighbors KNN), Patient data