How is your model doing?


A quick glance of your most important metrics.

Last 0 Evaluations
Accuracy
The proportion of all instances that are correctly predicted.
0.89 ▲ 0.7
minimum
threshold
Precision
The proportion of predicted positive instances that are correctly predicted.
0.93 ▲ 0.7
minimum
threshold
Recall
The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.
1.0 ▲ 0.7
minimum
threshold
F1 Score
The harmonic mean of precision and recall.
0.07 ▼ 0.7
minimum
threshold
AUROC
The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.
0.93 ▲ 0.7
minimum
threshold
Average Precision
The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.
0.98 ▲ 0.7
minimum
threshold

Last 0 Evaluations

How is your model doing over time?


See how your model is performing over several metrics and subgroups over time.

Multi-plot Selection:

Metrics

The moving average of all data points.
A measure of how dispersed the data points are in relation to the mean.
The proportion of all instances that are correctly predicted.
The proportion of predicted positive instances that are correctly predicted.
The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.
The harmonic mean of precision and recall.
The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.
The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.

Sex

Age

Datasets

Description

This dataset was created by combining different datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features. Every dataset used can be found under the Index of heart disease datasets from UCI Machine Learning Repository on the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/.

Version

  • Version 1

Features

  • Age
  • ChestPainType
  • Cholesterol
  • ExerciseAngina
  • FastingBS
  • MaxHR
  • Oldpeak
  • RestingBP
  • RestingECG
  • ST_Slope
  • Sex

Sensitive Data

  • Sensitive Data Used: ['Sex', 'Age']
    Justification: Demographic information like age and gender often have a strong correlation with health outcomes. For example, older patients are more likely to have a higher risk of heart disease.

Citation

  • @misc{fedesoriano, title={Heart Failure Prediction Dataset.}, author={Fedesoriano, F}, year={2021}, publisher={Kaggle} }

Reference

License

  • Identifier: CC0-1.0

Graphics

Quantitative Analysis

Accuracy
The proportion of all instances that are correctly predicted.
0.89 ▲ 0.7
minimum
threshold
Precision
The proportion of predicted positive instances that are correctly predicted.
0.93 ▲ 0.7
minimum
threshold
Recall
The proportion of actual positive instances that are correctly predicted. Also known as recall or true positive rate.
1.0 ▲ 0.7
minimum
threshold
F1 Score
The harmonic mean of precision and recall.
0.07 ▼ 0.7
minimum
threshold
AUROC
The area under the receiver operating characteristic curve (AUROC) is a measure of the performance of a binary classification model.
0.93 ▲ 0.7
minimum
threshold
Average Precision
The area under the precision-recall curve (AUPRC) is a measure of the performance of a binary classification model.
0.98 ▲ 0.7
minimum
threshold

Graphics

Fairness Analysis

Graphics

Model Details

Description

The model was trained on the Kaggle Heart Failure Prediction Dataset to predict risk of heart failure.

Version

  • Date: 2024-07-16
    Initial Release
    Version: 0.0.1

Owners

  • Name: CyclOps Team
    Contact: vectorinstitute.github.io/cyclops/
    Email: cyclops@vectorinstitute.ai

Licenses

  • Identifier: Apache-2.0

References

Name

Heart Failure Prediction Model

Model Parameters

Alpha

0.001

Learning_rate

adaptive

Loss

log_loss

Max_iter

1000

Penalty

l2

Average

False

Validation_fraction

0.1

Random_state

123

Shuffle

True

Verbose

0

Tol

0.001

Epsilon

0.1

Power_t

0.5

Warm_start

False

Eta0

0.01

L1_ratio

0.15

Class_weight

balanced

N_iter_no_change

5

Fit_intercept

True

Early_stopping

True

Considerations

Users

  • Hospitals
  • Clinicians
  • ML Engineers

Use Cases

  • Predicting risk of heart failure.
    Kind: primary
  • Predicting risk of pathologies and conditions other than heart failure.
    Kind: out-of-scope

Fairness Assessment

  • Affected Group: sex, age
    Benefits: Improved health outcomes for patients.
    Harms: Biased predictions for patients in certain groups (e.g. older patients) may lead to worse health outcomes.
    We will monitor the performance of the model on these groups and retrain the model if the performance drops below a certain threshold.

Ethical Considerations

  • The model should be continuously monitored for performance and retrained if the performance drops below a certain threshold.
    Risk: The model may be used to make decisions that affect the health of patients.