Content area
Full Text
Introduction
Machine learning, a branch of artificial intelligence (AI), is dedicated to developing algorithms and models that empower computers to learn from data and make informed predictions or decisions without explicit programming for each task. The fundamental concept behind machine learning is to enable systems to improve over time by learning from experience, thereby enhancing accuracy and efficiency with more data exposure. One of the most impactful applications of machine learning is in healthcare, where it plays a crucial role in disease diagnosis, treatment planning, and patient outcome prediction. In this study, we propose an optimized machine learning model specifically designed for breast cancer prediction. By leveraging data-driven learning techniques, this model aims to improve diagnostic accuracy and facilitate early detection, thereby contributing meaningfully to healthcare advancements. According to World Health Organization 2.3 million women received a breast cancer diagnosis in 2022, and 670,000 people died from the disease worldwide. In any nation on earth, breast cancer can strike women at any age after puberty, though its incidence rises with age.1
Main Contributions of the work are as follows
Ant Colony Optimization (ACO) and machine learning techniques are combined to forecast breast cancer.
Demonstrates the efficacy of ACO in a range of machine learning applications.
Improves generalization by optimizing classifier hyperparameters and increasing prediction accuracy.
Combines a number of methods with sophisticated optimization to propose a comprehensive framework.
The proposed model incorporates a range of widely used classifiers, including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest, Gradient Boosting, and XGBoost. These algorithms are chosen for their proven effectiveness in classification tasks and their ability to capture complex patterns within the data, making them well-suited for predictive modeling. After the initial model design, Ant Colony Optimization (ACO) is applied to fine-tune the hyperparameters, enhancing the model’s performance by optimizing key parameters. This combination of classifiers with ACO not only boosts accuracy but also strengthens the model’s reliability and robustness in identifying subtle data patterns.In swarm intelligence techniques, this algorithm is a member of the family of ant colony algorithms and is an example of a metaheuristic optimization. First put out by Marco Dorigo in his doctoral thesis in 1992.2Gradient Boosting and XGBoost are powerful ensemble techniques capable of high accuracy by...