Machine Learning Models in Breast Cancer Prediction: Performance Evaluation and Insights
D Hemalatha, N. Gomathi · 2025
As breast cancer is a leading issue across the world, efficient early detection and prediction mechanisms are needed more and even more. Merging machine learning techniques with the Internet of Things is promising for better accuracy in breast cancer prediction and for facilitating early therapies. To assist the desired prediction facility, the present work strives to offer a greater understanding of DNA-based breast cancer prediction and how IoT can be combined with a number of machine learning techniques. Understand how DNA gene profile can be used to build the best model for earliest prediction of Breast cancer achieving minimum error rate, inclusion of the Gene Profile in predict models to assist providers in predicting some of the signs of breast cancer early before the infection progresses and discussing how the IoT and diverse machine learning algorithms interact are the crucial steps carried out in making insights. In such a way, it is possible to intervene in time and promote patient outcomes by developing a personalized care plan. Algorithms can be deep learning methods such as Convolutional Neural Networks or supervised methods such as Random Forests or Support Vector Machines. The task of all these methods is data analysis to identify patterns and predict breast cancer risks or stages. There are some challenges associated with the use of predictive models developed with the help of IoT. These are, for example, critical problems of data security, concerns with interoperability, and the development of a standard to include devices. These factors need to be taken into consideration to ensure the usefulness of potential predictive systems.