Download Medical Data Classifier

Medical Data Classifier
undergraduate project
By: Avikam Agur and Maayan Zehavi
Advisors: Prof. Michael Elhadad and Mr. Tal Baumel
Motivation
• word2vec: An algorithm that associates
closely-related words.
• Combining with the outcome of our project, this
algorithm will help creating a medical text
summarizer.
Project Goals
● Create a fast, scalable, highly accurate,
machine-learning based classifier which
predicts whether a given document is medical
or not.
● Distributively run this classifier over a large
amount of web content and extract medical
documents.
Building a labeled dataset
Problem: Manually collect medical and nonmedical documents is almost impossible.
Solution: Using Wikipedia’s archive files, we
tagged wiki pages based on their category and title.
Result: decent amount of medical and non-medical
data.
Training Phase
Data transformation flow:
Documents
Boilerpipe
Tokenizing
TFIDF
Feature
selection
Training Phase
• Supervised learning using 2 classification
algorithms:
o Naive Bayes
o SVM
• Cleaning the data using:
o 𝜒 2 feature selection
o Stemming
Classifier Evalutaion - Measures
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ⋅ 𝑟𝑒𝑐𝑎𝑙𝑙
𝐹 − 𝑚𝑒𝑎𝑠𝑢𝑟𝑒 = 2 ⋅
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑟𝑒𝑐𝑎𝑙𝑙
Evalutaion Phase
Configuration Parameters:
• Classification algorithm
• Amount of features
• Stemming or not
Each configuration was trained and then tested on
a random 5% of the tagged dataset.
Results - Graph
Average F-Measure
0.87
0.86
0.85
0.84
0.83
0.82
0.81
0.8
0.79
0.78
0.77
Naïve Base
SVM
Features Count
Distributed Programming Phase
• Use Apache Spark framework
• Iterate ClueWeb web archives (~14 TB)
in a master-slave architecture
• Use the same training pipeline to convert
web document to vector
• Tag each document and export medicaltagged documents’ IDs.
Questions?