ML Algorithms Summary - Healthcare Focus

Linear Regression

✅ Pros

Simple and highly interpretable. Requires minimal computational power. Provides clear coefficient relationships.

❌ Cons

Assumes linear relationship between features and target. Sensitive to outliers and multicollinearity.

🎯 Healthcare Applications

Predicting patient recovery times, drug dosage optimization, cost forecasting for treatments.

Estimating blood pressure based on age and BMI, predicting hospital length of stay, modeling medication effectiveness over time, forecasting healthcare costs.

📋 Summary

Models linear relationships between medical variables and outcomes for predictive healthcare analytics and resource planning.

Logistic Regression

✅ Pros

Simple, fast, and interpretable. Provides probability scores for outcomes. Handles categorical variables well.

❌ Cons

Assumes linear decision boundary. May be outperformed by more complex models for non-linear relationships.

🎯 Healthcare Applications

Disease diagnosis, treatment success prediction, patient risk assessment, screening tests.

Diabetes screening, heart attack risk prediction, cancer detection from biomarkers, surgical outcome prediction, patient readmission risk.

📋 Summary

Predicts probability of binary medical outcomes (disease/no disease, success/failure) with interpretable coefficients.

Decision Trees

✅ Pros

Easy to understand and visualize. Can handle both numerical and categorical data. No assumptions about data distribution.

❌ Cons

Prone to overfitting. Can become overly complex. Sensitive to small changes in data.

🎯 Healthcare Applications

Clinical decision support, diagnostic pathways, treatment selection, patient triage.

Symptom-based diagnosis trees, treatment protocol selection, patient triage systems, medical guideline automation, risk stratification.

📋 Summary

Creates interpretable decision pathways that mirror clinical reasoning processes and medical decision trees.

Random Forest

✅ Pros

Robust to overfitting. Can handle large datasets with high dimensionality. Provides feature importance rankings.

❌ Cons

Less interpretable compared to single decision tree. Requires more computational power. Can be slow for real-time applications.

🎯 Healthcare Applications

Complex diagnostic tasks, ensemble predictions, biomarker discovery, electronic health record analysis.

Multi-disease risk assessment, genomic data analysis, drug discovery, EHR pattern recognition, medical image classification.

📋 Summary

Combines multiple decision trees for robust predictions in complex healthcare scenarios with built-in feature selection.

Naive Bayes

✅ Pros

Fast training and prediction. Works well with small datasets. Handles missing data gracefully. Provides probability estimates.

❌ Cons

Assumes feature independence (naive assumption). May not capture complex relationships between variables.

🎯 Healthcare Applications

Disease classification, text analysis of medical reports, spam detection in medical communications, risk assessment.

Medical document classification, symptom-based disease prediction, patient email triage, medical literature analysis, clinical text mining.

📋 Summary

Probabilistic classifier based on Bayes theorem, excellent for text analysis and medical document processing.

Support Vector Machines

✅ Pros

Effective in high dimensional spaces. Provides clear margin of separation. Robust to overfitting with proper regularization.

❌ Cons

Less effective on large datasets. Requires careful kernel choice and parameter tuning. Computationally intensive.

🎯 Healthcare Applications

High-precision classification, especially when classes are closely related or data is high-dimensional.

Cancer subtype classification, protein structure prediction, medical text categorization, gene expression analysis, biomarker classification.

📋 Summary

Finds optimal boundaries between different medical conditions or patient groups using sophisticated mathematical optimization.

ML Algorithms in Healthcare