heart attack prediction using wearables

Heart Attack Prediction

Heart Attack Prediction

-Ankita Singh and Harshitha Chidananda

● Heart attacks are the primary cause of increasing death-rate.

● Enormous amount of healthcare data is generated by various tracking devices (FitBit).

● Is there a way we could use it to predict health ailments (Heart Attacks)?

MOTIVATION

Aim● Leverage machine learning

algorithms to predict heart attacks.

● Propose a next generation wearable device to facilitate such a prediction.

Open Questions?● Is there a way to predict an ailment on the basis of the everyday activity?● Can the information provided by fitbit/other health trackers be used for

predicting a future health issue?● What extra features could be included to make a more informed analysis?

METHODOLOGY

● Use the data collected from various hospitals to create an ensemble model for heart attack prediction.

● Analyze the attributes of the data collected by FitBit.● Do a comparative study of the 2 datasets and propose additional attributes to be

included for the gen-next wearables.

DATASET

Dataset (Collected from hospitals)● UCI Machine Learning Repository-Heart Disease Dataset● Number of Records: 303● Number of features: 75● Features used: 13● Training: 250 Records● Testing: 53 Records

DATA PREPROCESSING

● Normalized the data to a standard normal distribution.● Used PCA as a feature extraction technique to select principal components .● Plotted the variance explained by the principal components to select the

necessary principal components.● 13 out of 75 features were selected to build the models.

PRINCIPAL FEATURES

● Age● Sex● Chest Pain Type● Resting blood pressure● Cholesterol● Fasting blood sugar(fbs)● Electrocardiographic results

● Maximum heart rate achieved(thalach)● Exercise induced angina● Depression induced by exercise● Slope (related to exercise behavior)● Number of vessels colored by fluoroscopy● Thal

MODELS● Decision Trees● SVM● Neural Networks● Ensemble (Bag Boosting)

BASELINE RESULTS

● Decision Trees - 69.8%● SVM- 75.47%● Neural Networks -

77.35%● Ensemble - 80.30%

FitBit Dataset● Used the fitbit public APIs to export self-generated physical activity.● A CSV file with a total of 17,568 observations.● Available Features

■ Heart Rate■ Number of steps walked■ Average elevation■ Calories burnt■ Sedentary minutes■ Body mass index■ Fat■ Sleeping Pattern

Missing Features

● Chest Pain Type● Resting blood pressure● Cholesterol● Fasting blood sugar(fbs)● Other specific cardiological Features

Feature Elimination

PROPOSED SOLUTION● Eliminating features which are

infeasible to be gathered using a wearable.

● Proposing new features to be added in FitBit

● Comparing the performance of the proposed wearable to the existing technology.

Proposed Features:

★ Blood Pressure(trestbps)★ Blood sugar(fbs)★ Resting electrocardiographic results★ Maximum heart rate achieved

Monitoring Blood Pressure● Until Sep 2014, FitBit provided an option to manually enter the monitored BP

readings into the health logs.● Existing wearables to monitor BP?

■ INDIEGOGO■ QUARDIOARM

● Integrate the data collected from these apps with the FitBit Logs.

INDIEGOGO

Monitoring Blood Sugar Level ● Basic members are given the ability to log three

glucose measurements per day.■ Morning■ afternoon ■ evening

● Premium Fitbit.com member (for $49.95 per year) you can log unlimited glucose measurements,

● But it does not gives the ability to make notes about the readings ("post meal" for example).

● Integrate with already existing Sugar monitoring wearables?

Resting Electrocardiographic Results + Max Heart Rate

QardioCore - A wearable which guarantees multi sensor ECG .

Prediction ResultsFeatures

● age● sex● trestbps● fbs● restecg● Max heart rate

Conclusion- We did an extensive analysis of the data collected from various hospitals to

predict the heart-attacks. (80.30%)- Analyzed the relevance of everyday data accumulated by FitBit.- Proposed a set of features to be supported by the existing hardware to facilitate

such health predictions.- Proposed model achieves an accuracy of 69.8%.

Future scope

- Other health issues- Improve accuracy- Design hardware- Integrating with existing

wearable

- Only 300 records.- Feasibility??- Reliability of the data

recorded by Fitbit.

Limitations

Strengths- Successful in predicting heart attack.- Synchronized with everyday activity patterns.- Easy and economical health monitorization.