D I G I T A L D I A G N O S T I C S
Herat Joshi , Data Science Lead at Great River Health , highlights how ensemble AI models improve early breast cancer detection by boosting accuracy and reducing invasive diagnostics , marking a major advance in precision medicine .
Breast cancer remains one of the leading causes of cancer-related deaths worldwide . According to the World Health Organization ( WHO ), breast cancer is the most common cancer among women globally . Recent data indicates that approximately one in eight women ( 12.5 %) worldwide will be diagnosed with breast cancer in their lifetime , highlighting the critical need for early detection and accurate diagnosis . Traditional methods , while effective , often face limitations , including high rates of false positives and negatives , class imbalances in data and the invasive nature of some diagnostic procedures . With the increasing availability of large datasets and advancements in Artificial Intelligence ( AI ), the medical community has begun turning to Machine Learning ( ML ) as a powerful tool to enhance diagnostic precision and improve patient outcomes . Among these advancements , ensemble AI techniques represent a significant leap forward in breast cancer detection , offering a more reliable and accurate method to identify malignant tumours at an earlier stage .
Challenges in breast cancer detection
Diagnosing breast cancer is fraught with challenges . One of the biggest issues is class imbalance in breast cancer data . This occurs when most cases are benign , making it harder for algorithms to identify malignant cases accurately . This imbalance can lead to false negatives – where cancer is missed – or false positives , subjecting patients to unnecessary procedures and stress .
Timeliness is another concern . Early detection is crucial to improving survival rates , but traditional diagnostic methods can be slow , delaying treatment and reducing the chances of a better outcome .
The power of ensemble Machine Learning models
Ensemble Machine Learning models work like a panel of experts collaborating to solve a problem . Instead of relying on a single model ’ s prediction , ensemble models combine the insights of several models , resulting in a more accurate and reliable diagnosis .
In my research on improving diagnostic tools for women ’ s health , I developed an ensemble model that incorporates multiple Machine Learning techniques , including CatBoost , XGBoost , Random Forest and Linear Discriminant Analysis ( LDA ). This combination resulted in an impressive classification
ENSEMBLE AI TECHNIQUES FOR EARLY BREAST CANCER DETECTION : A LEAP FORWARD IN PRECISION MEDICINE
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