Bone Cancer Prediction Using Regression Algorithms and Machine Learning Approaches

Bone cancer is an aggressive disease that is often difficult to diagnose and treat and can be very dangerous for the patient. Bone cancer needs to be predicted in time and accurately for successful treatment and good patient care. This study aims to introduce a bone cancer prediction model that combines regression algorithms and machine learning techniques. The model reads data from patient medical records, such as age, sex, or genetic markers, and trains on it to predict the probability of bone cancer. We have tested linear regression, logistic regression, random forest multiple regression algorithm artificially neural networks, and support vector Machine Learning. We used an extensive bone cancer cases and controls dataset from different research centers and hospitals. 6 Our preliminary results show that the new method is more accurate in predicting bone cancer than traditional methods. Regrettably, regression algorithms and machine learning techniques are good enough to model many variables at once while detecting intricate patterns in the data, so as a result, the ability to predict is enhanced. Their model can help healthcare professionals identify PD earlier and make well-informed early treatment choices to improve patient outcomes and quality of life. This is a critical goal given the progressive nature of manystage synucleinopathies. However, more studies and validation are needed before implementing the model clinically.