Machine Learning may Revolutionize accurate diagnosis of Cardiac Tumor, unravels study

The study findings were as follows:

• The RF model emerged as the superior ML model, achieving a groundbreaking accuracy of 96.25% and a perfect ROC AUC score of 0.99. This performance significantly outperformed existing diagnostic approaches.

• Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability. The RF model demonstrated a diagnostic accuracy of 94% in a real-world setting.

• Key predictors identified included age, echo malignancy, and echo position, underscoring the value of integrating diverse data types for enhanced diagnostic precision.

The study highlights the potential of ML to revolutionize cardiac tumor diagnostics. The RF model's superior performance and clinical validation results underscore the feasibility of implementing ML-driven diagnostic tools in clinical settings. The integration of diverse data types, such as echocardiography and pathology data, plays a crucial role in enhancing diagnostic accuracy. This study highlights the capabilities of ML in improving diagnostic precision for cardiac tumors but also paves the way for its broader application across various medical diagnostics domains.

Reference:

Sadegh-Zadeh, S.-A., Khezerlouy-aghdam, N., Sakha, H., Toufan, M., Behravan, M., Vahedi, A., Rahimi, M., Hosseini, H., Khanjani, S., Bayat, B., Ali, S. A., Hajizadeh, R., Eshraghi, A., Ghidary, S. S., & Saadat, M. (2024). Precision diagnostics in cardiac tumours: Integrating echocardiography and pathology with advanced machine learning on limited data. Informatics in Medicine Unlocked, 101544, 101544. https://doi.org/10.1016/j.imu.2024.101544