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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Data Science in Medicine: Precision & Recall or Specificity & Sensitivity?

Badrinarayan M
Badrinarayan M Last Updated : 14 Jun, 2024
6 min read

Introduction

Evaluation of models and medical tests is significant in both data science and medicine. However, these two domains use different metrics, which is confusing. While data scientists use precision and recall, medics use specificity and sensitivity. When it comes to the relationship between these metrics, they differ. This, therefore, calls for comprehension of their disparities and applications to evaluate models accurately and have an effective exchange between data scientists and medical professionals.

Overview

  • The blog contrasts data science metrics (precision, recall) with medical metrics (specificity, sensitivity) for model evaluation.
  • Precision measures the accuracy of positive predictions, while recall (sensitivity) assesses the detection of all actual positives.
  • Specificity evaluates the accuracy of negative predictions, which is crucial for identifying true negatives in medical tests.
  • Practical examples illustrate the implications of different metric combinations in medical screenings and disease detection.
  • Balancing precision and recall using the F1 score is recommended for comprehensive model performance evaluation.
Data science in Medicine

Table of contents

Data Science Metrics

Precision is the ratio or fraction of true positives out of all positive examples predicted by a model. It answers this question: “Out of all the examples predicted as positive, how many are positive?”

Data Science in Medicine

The total number of cases that were positively classified based on their actual existence as HIV/AIDS can be measured using precision.

Precision tells us how many of the positively classified instances were positive. For instance, in a spam detection system, precision calculates the proportion of emails marked as spam that are spam.

Recall, also called sensitivity in the field, measures the fraction of all true positives divided by total actual positive cases. It addresses the question: “Of all positive cases, how many were correctly predicted as positives?”

Data Science in Medicine

Recall refers to the model’s ability to find all relevant instances. For example, in the case of a medical test for a disease, recall tells us how many actual positive cases (patients with the disease) were correctly identified by the test.

Also read: Machine Learning & AI for Healthcare in 2024

Medical Metrics

Specificity calculates the ratio of true negatives predicted and those that are negative. It seeks to answer the question, ” How many negative predictions for people who do not have a condition are correct?”

Data Science in Medicine

Specificity measures how well a test can tell a negative. In other words, during medical screening, specificity shows how many healthy individuals who do not have the disease may be correctly identified as externalities.

Sensitivity (or recall in data science) measures the proportion of true positive predictions out of all positive cases. It answers the same question as recall.

Data Science in Medicine

Comparing Metrics

Precision Compared to Specificity

Precision and specificity cover different elements of model performance. Precision focuses on the accuracy of positive predictions, asking how many of the predicted positives are actually positive. Specificity evaluates the accuracy of negative predictions, indicating how well the model identifies negative cases.

For example, in a medical test for a rare disease, high precision means that most positives identified actually have it, while high specificity means that most negatives are correctly classified as not having it.

Recall vs Sensitivity

The same metric has two different names: recall and sensitivity. Both measures describe how many true positives the model identifies. Both metrics measure the ability to find positive instances, like detecting all patients with disease.

Also read: Extracting Medical Information From Clinical Text With NLP

Practical Examples

To illustrate the differences and importance of these metrics, consider the following examples:

Example 1: Low Precision, High Recall, High Specificity

In this scenario, if the classifier predicts negatively, the prediction is trustworthy (high specificity), but a positive prediction is less reliable (low precision). However, the model effectively identifies all positive cases (high recall).

This type of classifier might be used in initial medical screenings where it is crucial not to miss any positive cases, even if it means having more false positives.

Example 2: High Precision, High Recall, Low Specificity

Here, the classifier predicts everything as positive. While it identifies all actual positives (high recall) and most predictions are correct (high precision), it fails to identify negatives (low specificity).

This scenario might occur where missing a positive case is highly undesirable, such as in critical disease detection, but where the cost of false positives is relatively low.

Example 3: High Precision, Low Recall, High Specificity

This classifier is reliable when it predicts a positive case (high precision), but it misses many actual positives (low recall). It correctly identifies most negatives (high specificity).

Such a classifier could be used when confidence in positive predictions is crucial, such as diagnosing a condition requiring highly invasive or risky treatment.

Also read: Application of Machine Learning in Medical Domain!

Choosing the Right Metric

The correct metric depends on the particular application and the relative costs of false positives and negatives:

  • In terms of precision, it is more crucial to minimize false positive results when they are more important. For instance, in email spam detection, it is better to have some spam messages in your inbox than to classify them as important spam emails.
  • Recall (Sensitivity) matters most if false negatives are important. For example, in medical diagnostics, missing out on a positive case like a disease can be devastating; hence, it is better to generate some wrong results that can be eliminated through further tests.
  • Specificity assumes significance when the price of false positives becomes unbearable. For instance, in drug testing, false positives should be avoided so as not to punish innocent people.

Balancing Metrics

One has to strike a balance among these ratios. For instance, F1 score is one of the metrics that combine both precision and recall to give an overall accuracy of a test that balances the trade-off between precision and recall.

F1 score is highly recommended to attain this equilibrium between precision and recall, especially when imbalanced classes are involved.

Conclusion

Understanding and appropriately applying precision, recall, specificity, and sensitivity are vital for developing and evaluating data science and medicine models. Each metric provides unique insights into model performance, and choosing the right one depends on the specific context and the consequences of errors. By bridging the gap between these fields, we can improve communication and collaboration, ultimately enhancing the effectiveness of predictive models in medical applications.

In summary, while precision and recall are often emphasized in data science and specificity and sensitivity in medicine, recognizing their relationships and differences allows for more nuanced and accurate model evaluations. This understanding can significantly impact the development of better diagnostic tools and predictive models, leading to improved patient outcomes and more efficient medical practices.

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Frequently Asked Questions

Q1. What is the difference between precision and recall?

A. Precision measures the accuracy of positive predictions, while recall (sensitivity) assesses the ability to identify all actual positive cases.

Q2. How do specificity and sensitivity relate to medical testing?

A. Specificity evaluates the accuracy of negative predictions, indicating how well a test identifies true negatives, whereas sensitivity (recall) measures the proportion of true positives correctly identified.

Q3. Why are different metrics used in data science and medicine?

A. Data scientists focus on precision and recall to assess model performance, while medical professionals use specificity and sensitivity to evaluate diagnostic tests, reflecting their different priorities in error management.

Q4. When should high specificity be prioritized over high precision?

A. High specificity is crucial when it is important to accurately identify true negatives, such as in medical screenings where false positives can lead to unnecessary anxiety and additional testing.

Q5. What is the F1 score, and why is it important?

A. The F1 score is a metric that balances precision and recall, providing an overall measure of a model’s accuracy, especially useful when dealing with imbalanced classes.

Badrinarayan M
Badrinarayan M

Data science Trainee at Analytics Vidhya, specializing in ML, DL and Gen AI. Dedicated to sharing insights through articles on these subjects. Eager to learn and contribute to the field's advancements. Passionate about leveraging data to solve complex problems and drive innovation.

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