After working on breast tumour detection with CNNs and malnutrition analysis in Bangladesh, I’ve accumulated hard-won lessons about applying machine learning to healthcare data.
Lesson 1: Data Quality Beats Model Complexity
In all my healthcare projects, the biggest gains came from data cleaning, not switching to a more complex model. Medical data is messy — missing values, measurement errors, inconsistent coding, label noise from disagreeing clinicians.
Spending 60% of your time on data quality will almost always yield better results than spending that time on model architecture.
Lesson 2: Class Imbalance is Almost Always a Problem
In medical diagnosis — disease detection, fraud, anomaly — the thing you’re trying to detect is rare by definition. Naive accuracy metrics are meaningless when 99% of cases are negative. Always report sensitivity, specificity, AUC, and calibration curves.
Lesson 3: Interpretability Often Matters More than Performance
A model that achieves 99% AUC but can’t explain its decisions is often useless in clinical settings. Clinicians need to understand why the model flagged a case. SHAP values, LIME, and attention visualization have saved more of my models from the bin than any hyperparameter tuning.
Lesson 4: Statistical Rigor Still Matters in the ML Era
Cross-validation is not a substitute for proper experimental design. When reporting results, always include confidence intervals, and try to replicate findings on an independent cohort when possible.
Looking Forward
Healthcare ML is still in its early stages. The models that will actually help patients are those built with humility, rigor, and genuine collaboration with domain experts.