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Reading list

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

Breaking Down Social Bias in Artificial Intelligence Algorithms for Cardiovascular Risk Assessment

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 05 Jun, 2023
3 min read

Cardiovascular disease (CVD) prevention is crucial for identifying at-risk individuals and providing timely intervention. However, traditional risk assessment models like the Framingham Risk Score (FRS) have shown limitations, particularly in accurately estimating risk for socioeconomically disadvantaged populations. Hence, researchers are now exploring the potential of artificial intelligence (AI) and machine learning (ML) algorithms to improve risk assessment. A recent groundbreaking study shows the use of AI algorithms to enhance cardiovascular risk assessment. What makes it better than traditional models is that it addresses the inherent bias in the FRS.

Also Read: Decoding the Blueprint of Life: AI’s Geneformer

Artificial Intelligence (AI) & Machine Learning (ML) algorithms can now assess the risk of CVD better than the Framingham Risk Score (FRS).

The Framingham Risk Score: Assessing Cardiovascular Risk

The Framingham Risk Score is a well-known algorithm to estimate an individual’s 10-year cardiovascular risk. Originally developed based on data from the Framingham Heart Study, the FRS primarily focuses on estimating the risk of developing coronary heart disease. However, it has been observed that the FRS may not accurately assess risk for specific demographic groups, such as those facing socioeconomic disadvantages.

Potential of AI and ML in Risk Assessment

Artificial intelligence and machine learning technologies offer promise in addressing the equity gaps and biases found in traditional risk assessment models. By leveraging vast amounts of data and complex algorithms, AI has the potential to provide more accurate risk estimates and improve patient outcomes. Nevertheless, a critical appraisal of these algorithms is necessary before integrating AI-informed decision-making into clinical practice.

Also Read: AI Discovers Antibiotic to Combat Deadly Bacteria

Unveiling Bias: Analyzing ML Algorithms

This study aims to employ an equity lens to identify and understand sources of bias within ML algorithms that aim to improve cardiovascular risk assessment relative to the FRS. Researchers will focus on aspects like race/ethnicity, gender, and social stratum to assess the extent of bias within these algorithms. The goal is to uncover and address any unfair discrepancies that may arise in the risk assessment process.

A comprehensive literature search will be conducted to address whether AI algorithms designed to estimate CVD risk and compare performance with the FRS can effectively address bias. Databases such as MEDLINE, Embase, and IEEE will be scoured for relevant studies. The search will not impose any date filters, although it will apply English language filters.

Eligibility Criteria for Inclusion

The study will consider papers describing specific algorithms or ML approaches that provide risk assessment outputs for conditions such as coronary artery disease, stroke, cardiac arrhythmias (specifically atrial fibrillation), heart failure, or global CVD risk scores. To maintain the focus on prevention, the study will not include papers discussing algorithms solely for the diagnosis of CVD.

Also Read: Groundbreaking News: FDA Grants Approval to Elon Musk’s Neuralink for Human Trials

Analyzing and Reviewing the Literature

The included studies will go through a thorough, structured narrative review analysis. This analysis will clarify the potential biases present within the AI algorithms with regard to the FRS. It will shed light on the effectiveness of these algorithms in mitigating bias and improving cardiovascular risk assessment. The study has received an ethics exemption from the General Research Ethics Board at Queen’s University.

Sharing Findings and Future Steps

A peer-reviewed journal will accept the completed systematic review for publication. Additionally, key findings from the study will be presented at relevant conferences. Researchers aim to drive discussion, collaboration, and further advancements in AI algorithms for cardiovascular risk assessment by disseminating these results.

Also Read: Breaking Barriers: ChatGPT’s Radiology Exam Triumph and Limitations Unveiled!

Our Say

The study aims to address the social biases inherent in the Framingham Risk Score by exploring the potential of AI & ML algorithms in cardiovascular risk assessment. By critically analyzing existing literature and uncovering sources of bias, researchers hope to pave the way for fair and accurate risk assessment tools that can benefit all individuals, regardless of socioeconomic background. This study represents a significant step towards improving healthcare equity and enhancing cardiovascular disease prevention. It also opens new avenues for the use of artificial intelligence and machine learning in healthcare.

AIAI in healthcareAI modelsalgorithmsArtificial IntelligencebiasMLnewsrisk assessment
K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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