Exploring machine learning for breast cancer classification and the potential role of HAMLET in cancer treatment

Abstract

In India, with a population exceeding 100 million, the prevalence of cancer is a significant health concern, affecting around 2.5 million individuals annually. There are more than 800,000 new cases and 5.5 lakh fatalities reported each year, with a concerning 70\% of patients reaching a critical stage, resulting in low survival rates. The escalating population growth, coupled with medical research advancements, underscores the critical need for early detection.

Breast cancer has seen exponential growth, making it the second most serious malignancy among identified cancers. Recent studies attribute this rise to factors such as the lack of breastfeeding during infancy. Notably, human alpha-lactalbumin (HAMLET), found in breast milk, has emerged as a potential key player in combating cancer. Studies indicate that HAMLET can kill cancer cells and provide protection against cancer by controlling tumor cells detected in breast milk. Medical research from the US National Library suggests that HAMLET induces cancer cell death in a manner akin to programmed cell death, showcasing excellent anti-tumor efficacy.

This study explores the breast cancer classification utilizing machine learning algorithms. Logistic Regression and Random Forest were employed on a comprehensive dataset. Results indicate promising accuracy rates, demonstrating the efficacy of machine learning in aiding breast cancer diagnosis. HAMLET, identified as Human Alpha-lactalbumin Made Lethal to Tumor Cells, stands out as an anticancer gene found in human milk, offering potential effectiveness in cancer treatment and disrupting new cancer pathways.