ML Algorithms Guide for Healthcare

SUPERVISED LEARNING

Learns from labeled medical data to predict diagnoses, outcomes, and treatments

Linear Regression

Predicts patient outcomes, drug dosages, and biomarker levels

Logistic Regression

Disease diagnosis, treatment response prediction, risk stratification

Decision Trees

Clinical decision support, treatment pathway optimization

Random Forest

Multi-disease prediction, feature importance in medical data

SVM

Cancer classification, medical image analysis, drug discovery

Naive Bayes

Symptom-based diagnosis, drug interaction prediction

UNSUPERVISED LEARNING

Discovers hidden patterns in medical data, patient subgroups, and disease subtypes

K-Means

Patient stratification, disease subtype discovery, biomarker grouping

Hierarchical

Gene expression clustering, disease progression analysis

DBSCAN

Outlier detection in medical data, rare disease identification

PCA

Genomic data analysis, medical imaging compression, biomarker discovery

t-SNE

Single-cell RNA-seq visualization, drug response clustering

Association Rules

Drug interaction discovery, comorbidity pattern analysis

REINFORCEMENT LEARNING

Optimizes treatment strategies through trial-and-error learning with patient outcomes

Q-Learning

Personalized treatment optimization, drug dosing strategies

Policy Gradient

Adaptive treatment protocols, clinical trial optimization

Actor-Critic

Dynamic treatment planning, resource allocation in hospitals

Monte Carlo

Treatment outcome simulation, risk assessment modeling

DEEP LEARNING

Advanced neural networks for medical imaging, genomics, and complex healthcare pattern recognition

CNN

Medical imaging analysis, radiology, pathology, dermatology

RNN

Electronic health records, time-series vital signs, drug sequences

LSTM

Patient monitoring, disease progression tracking, treatment response

GAN

Synthetic medical data generation, data augmentation, privacy preservation

Transformer

Clinical text analysis, drug discovery, protein structure prediction

Autoencoder

Medical data denoising, anomaly detection, feature learning

HEALTHCARE ML ALGORITHMS: COMPLEXITY & CHARACTERISTICS

Algorithm	Time Complexity	Space Complexity	Interpretability	Training Speed	Prediction Speed	Healthcare Use Case
Logistic Regression	O(n²)	O(n)	High	Fast	Fast	Disease diagnosis, risk assessment
Random Forest	O(n log n)	O(n)	Medium	Medium	Fast	Multi-disease prediction, biomarker discovery
SVM	O(n³)	O(n²)	Medium	Slow	Fast	Cancer classification, medical imaging
K-Means	O(nkdi)	O(nk)	High	Fast	Fast	Patient stratification, disease subtypes
CNN	O(epochs×batch×layers)	O(parameters)	Low	Very Slow	Medium	Medical imaging, radiology, pathology
LSTM	O(sequence×hidden²)	O(parameters)	Low	Slow	Medium	Patient monitoring, EHR analysis

🏥 Medical Imaging & Radiology

CNN (X-ray, MRI, CT scan analysis)
U-Net (medical image segmentation)
GAN (synthetic medical images for training)
Transfer Learning (pre-trained models for medical images)

🧬 Genomics & Precision Medicine

Random Forest (genetic variant classification)
PCA (genomic data dimensionality reduction)
Hierarchical Clustering (gene expression analysis)
Deep Learning (protein structure prediction)

📊 Electronic Health Records (EHR)

LSTM (temporal pattern recognition)
Logistic Regression (disease risk prediction)
Association Rules (comorbidity pattern discovery)
Transformer (clinical text analysis)

💊 Drug Discovery & Development

Graph Neural Networks (molecular property prediction)
Reinforcement Learning (drug optimization)
Autoencoder (molecular representation learning)
Monte Carlo (drug interaction simulation)

🩺 Clinical Decision Support

Decision Trees (treatment pathway optimization)
SVM (disease classification)
Naive Bayes (symptom-based diagnosis)
Ensemble Methods (multi-algorithm consensus)

📈 Patient Monitoring & Outcomes

Time Series Analysis (vital signs monitoring)
K-Means (patient stratification)
Survival Analysis (treatment outcome prediction)
Anomaly Detection (early warning systems)

MACHINE LEARNING ALGORITHMS FOR HEALTHCARE