Analysing Breast Cancer Risk Through Food Habits Using CNN and LSTM Models

Breast cancer is a major global health concern and one of the leading causes of death among women. Its risk is influenced by both genetic and environmental factors, with diet recognized as a key modifiable contributor. This study focuses on the role of vegetarian and non-vegetarian food habits in influencing breast cancer progression and applies deep learning models to predict risk outcomes. Objective: The primary objective is to analyze how different dietary patterns, particularly plant-based versus animal-based diets, affect breast cancer risk and progression. Methods: A dataset of breast cancer patients was classified into vegetarian and non-vegetarian groups. Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models were employed to predict patient risk levels and identify hidden patterns linking dietary intake with cancer growth. Findings: The results reveal that non-vegetarian diets, especially those high in fats, red meat, and processed foods, are associated with increased cell growth and higher cancer risk. In contrast, vegetarian diets rich in fiber, antioxidants, and phytochemicals demonstrate a protective effect. CNN achieved the highest accuracy, while LSTM effectively captured sequential dietary behaviors. These findings confirm that deep learning can serve as a valuable tool for diet-based risk prediction in breast cancer patients.