Advanced Applied

Machine Learning for Healthcare

Applying ML to Healthcare Problems and Clinical Decision-Making

Learning Objectives

  • Apply ML techniques to healthcare use cases
  • Understand clinical data types and preprocessing
  • Evaluate ML models in healthcare contexts
  • Address ethical and regulatory considerations

Healthcare ML Applications

Disease Prediction

Early detection of diabetes, heart disease, cancer using patient data and biomarkers.

Medical Imaging

Automated diagnosis from X-rays, MRI, CT scans using deep learning models.

Drug Discovery

Accelerating drug development through molecular modeling and prediction.

Personalized Medicine

Tailoring treatment plans based on patient genetics and clinical history.

Case Study: Diabetes Prediction

Problem Statement

Build a predictive model to identify patients at high risk for Type 2 diabetes based on clinical measurements and demographic data.

Data Features

  • • Glucose levels, BMI, blood pressure
  • • Age, family history, lifestyle factors
  • • Previous medical history

ML Approach

  1. 1. Data Collection: Electronic Health Records (EHR)
  2. 2. Preprocessing: Handle missing values, normalize features
  3. 3. Model Selection: Logistic Regression, Random Forest, XGBoost
  4. 4. Evaluation: AUC-ROC, Precision, Recall, F1-Score
  5. 5. Clinical Validation: Collaborate with healthcare professionals

Code Demo Setup

# Python libraries for healthcare ML
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, roc_auc_score
# Load healthcare dataset
data = pd.read_csv('diabetes_data.csv')
# Feature engineering and model training...

Use Cursor AI to generate complete implementation with explanations

Ethical Considerations

Bias & Fairness

Ensure models don't discriminate based on race, gender, or socioeconomic status

Privacy & Security

Protect patient data with encryption, anonymization, and HIPAA compliance

Transparency & Interpretability

Use explainable AI methods to help clinicians trust and understand predictions

Student Assignment

Project: Build a disease prediction model using real healthcare dataset

Deliverables:

  • • Data analysis and visualization report
  • • Trained ML model with performance metrics
  • • Discussion of ethical considerations
  • • Recommendations for clinical deployment

Tools: Python, Jupyter Notebook, Scikit-learn, Pandas