Machine Learning Model Outperforms Pulmonologists in Detecting Lung Cancer, suggests research

Key Findings:

Model Performance:

• The DES model resulted in an area under the receiver operating characteristic (ROC) curve of 0.77±0.01.

• Sensitivity: 76.2%±2.04%, 6.5% better than the average pulmonologist.

• Specificity: 63.8%±2.3%.

• Positive predictive value (PPV): 41.6%±1.2%.

• F1-score: 53.8%±1.0%.

Comparison with Pulmonologists:

• DEs model outperformed all the five pulmonologists, while sensitivity was exceptionally high, particularly for early-stage detection.

Significant Biomarkers:

• Smoking status, LDH, serum calcium levels, low sodium levels, leucocytes, neutrophil count and CRP values were the strongest predictors of LC.

The DES ML model clearly outperformed pulmonologists in detecting lung cancer with higher sensitivity and identification of critical biomarkers for risk stratification. These findings show the ability of ML in changing the current landscape of early detection strategies for improving outcomes for patients at risk of LC. ML-driven models integrated into health care could provide decision-making support to clinicians and may decrease the rate of late-stage cancer diagnosis.

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