Decision Support in Heart Disease Prediction System using Naïve Bayes

1. Decision Support in Heart Disease Prediction System using Naïve Bayes Presented by Robert Karam March 19, 2014

2. Background – EHR & CDSS • Electronic Health Records: Collection of patient or population health information • Clinical Decision Support System: The intelligent use of EHRfor better patient care, assist healthcare providers at the point of care, either pre- or post-diagnosis, or during treatment • CDSS has increased the value of EHR, but it can be improved • A major topic in artificial intelligence in medicine

3. Clinical Decision Support • CDS systems can answer simple questions • Basic patient or population statistics • Drug interactions / cross referencing • E.g. Average age of patient with heart disease? How many surgeries resulted in hospital stays greater than 10 days? Etc. • ..but EHRs contain a large amount of data • More intelligent CDS systems may capitalize on this! • Key: use Data Mining techniques to discover hidden trends, patterns, and useful knowledge within the EHRs

4. Naïve Bayes (Refresh) • Bayes’ Theorem provides a relationship between the probabilities of events A and B, and the conditional probabilities of A given B, and B given A • A Naïve Bayes Classifier assumes total independence between events (or features, in this case) • Performs relatively well in common, real-world (i.e. complex) problems despite its simplicity

5. Overview of Decision Support System • Implemented as a web application / questionnaire • Gathers patient information that may be relevant to diagnosing heart disease • e.g. patient age, sex, type of chest pain, blood sugar, EKG abnormalities, maximum heart rate, smoking, serum cholesterol, etc. • 15 features used in all • Designed to aid physician in determining best course of action • Uses a training database of historical heart disease diagnoses • Database offers a larger pool of ‘experience’ for that physician (i.e. they may have seen 100 heart disease patients and could make a judgement based on that, but this provides another 1000 samples) • Outputs a binary diagnosis (no: < 50% diameter narrowing, or yes: >50% diameter narrowing)

6. Why NBC? • Patients are different, so feature independence is important • NBC… • Works well with high dimensional data • Computation is trivial (when independence assumed!) • Relatively few samples required for parameter (evidence) estimation • However, • Quality of diagnosis depends on quality of training data • May be outperformed by other classifiers (e.g. decision trees or SVM) • 15 features selected – is that enough? Patient history + family history? • As a diagnosis tool, it may sufficiently aid healthcare practitioners regardless

7. Discussion • Diagnosis is a multi-faceted problem, many different aspects involved • Knowledge discovery in EHR databases may yield a wealth of information and support • Many unanswered questions: • No results presented or examples of use (real or synthetic data?) • No mention of model performance • No description (size?) of training database. • No details given why these 15 (of 60+) features were selected • All patients are different, but advanced data mining algorithms and techniques may work around this problem to provide smarter tools to aid physicians in targeted patient care