This is a short 3 slider presentation giving the glimpse of steps taken to improve the current sleep apnea treatment adherence techniques by using available state of the art Machine learning models. This is the work done by me under Prof Jaideep Srivastava at the University of Minnesota.

1. Topic: Churn Prediction for Sleep Apnea
study.
--Rahul Bhojwani (bhojw005@umn.edu)
Data Definition
● Daily device usage time
● Daily Avg Pressure readings from the device
● Daily Apnea Hypopnea index
● Daily device leakage readings
● Patients demographic data
● Diagnosis test results from timely hospital checkups
Problem Definition:
● Sleep Apnea Study at Fairview Hospital.
● ~22 million Americans suffer from sleep apnea.
● More than 200,000 US cases registered per year
● CPAP device prescribed
● Patient should stick for at least 6 months
● In General adherence is ~40-50%. Hospital resources
are making calls helping it increase to ~65%.
GOAL:
Predict as early as possible whether a patient will adhere to the
CPAP device usage. Churn is defined based on the usage in
151-180th day.

2. ● Model design:
○ 30 days -> 180 days (Habit Problem)
○ 13 days -> 180 days
● Data exploration and Feature extraction:
○ Raw time series
○ Mean and SD over intervals
○ Change in mean / slope
○ Demographic data
○ % days adherence
● Imbalanced class problem:
○ Undersampling
○ Oversampling- SMOTE
○ Balanced Cascade
● Feature Selection:
○ Anova
○ Chi-square
○ L1
○ RFE
● Models experimented with, on structured
data:
○ Decision Tree
○ SVM
○ Random Forest
○ XGBoost
○ Deep learning Feed forward Network
● Optimized for:
○ F1 score
○ npv, ppv
○ Cohen Kappa

3. ● Time Series Classification:
○ 3 daily time series of:
■ Usage time
■ Device Pressure
■ AHI events
○ Demographic and diagnosis data
● Time series similarity:
○ Cosine similarity with average time series of each
class. ( Problem coz of varying time series)
○ Smoothed time series cosine similarity.
■ Expanding mean
● Deep Learning models:
○ LSTM for time series classification
○ CNN
○ Long Short Term Memory-Fully Convolutional
Neural Network (LSTM-FCNN)
(https://arxiv.org/abs/1709.05206)
○ Attention Long Short Term Memory-Fully
Convolutional Neural Network (ALSTM-FCNN)
(https://arxiv.org/abs/1709.05206)
● Conclusion:-
○ Using the data of first 13 days, we can predict the
engagement condition after 6 months with a decent
accuracy
○ In initial 13 days of CPAP prescription, decision to
continue with the treatment or suggesting an alternative
one can be taken.