Cancer Disease Prediction using Machine Learning

Abstract

Cancer is still among the leading causes of death on the planet. Poor outcomes and higher mortality rates create an urgent need for early diagnosis using high-accuracy cancer-detecting methods. Some diagnostic methods in use include biopsy, imaging, and clinical evaluation. These techniques are highly expensive, time-consuming, and prone to errors due to human involvement. With the advancement of artificial intelligence, machine learning has become one of the strongest tools for predicting, classifying, and diagnosing cancer. Machine learning algorithms will therefore analyze a large volume of patient data, including genetic information, medical images, and patient clinical history, for any unusual patterns that may not be easily spotted by experts. Useful algorithms include Decision Trees, Random Forest, Support Vector Machines, K-Nearest Neighbours, and Deep Learning. This study will focus on selecting appropriate algorithms, preprocessing, and feature extraction to achieve high prediction accuracy. Challenges also persist in data imbalance, overfitting, and ensuring patient data confidentiality. However, the integration of machine learning into cancer diagnosis has been promising, enabling higher early detection rates, supporting personalised treatment planning, and promoting low-cost, efficient, and patient-centred health care solutions.