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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

AI Takes the Lead in Prenatal Care: Predicting Fetal Heart Rate with Precision

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 07 Jun, 2023
3 min read

Researchers have recently made groundbreaking progress in the field of machine learning (ML) by developing methods that accurately identify predictors associated with fetal heart rate changes in pregnant patients undergoing neuraxial analgesia. This revolutionary study, published in BMC Pregnancy and Childbirth, sheds light on the importance of using ML algorithms to predict and manage potential health risks during labor. Let’s dive into the details and explore how these findings can revolutionize prenatal care.

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

Understanding Neuraxial Analgesia and Fetal Heart Rate Changes

Neuraxial analgesia is a widely-used labor pain management method in the United States. It includes techniques such as spinal, epidural, and combined spinal-epidural (CSE). While effective in providing pain relief, this method has been associated with fetal heart rate changes. Although some changes may resolve naturally, a significant drop in heart rate, known as fetal bradycardia, can indicate potential health problems for the baby. Identifying fetal bradycardia predictors becomes crucial in effectively managing and addressing these risks.

Machine learning (ML) algorithms lead in prenatal care by predicting fetal heart rate changes in pregnant patients with neuraxial analgesia.

Harnessing the Power of Machine Learning

Recognizing the complex nature of fetal bradycardia and its potential predictors, the researchers turned to ML as a powerful tool. ML algorithms excel at analyzing vast amounts of data & identifying patterns that may not be visible through traditional analysis methods. By using ML, researchers can manage multiple predictor variables and uncover unknown patterns that may contribute to fetal heart rate changes.

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

Unveiling Unknown Patterns and Improving Accuracy

One of the significant advantages of ML algorithms is their ability to uncover unknown patterns and relationships between predictors and outcomes. Unlike humans, ML algorithms don’t make assumptions about linear relationships, leading to improved accuracy. By leveraging ML algorithms, the research team aimed to design models capable of accurately identifying predictors of fetal heart rate changes.

ML & AI applications in prenatal care.

The Study and Findings

To validate their approach, the researchers conducted a retrospective analysis involving 1,077 healthy laboring patients who received neuraxial analgesia. They compared the performance of four models: principal components regression, random forest, elastic net model, and multiple linear regression. The random forest model emerged as the most accurate, surpassing the others in terms of prediction accuracy using mean squared error (MSE).

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

Identifying Key Predictors

The analysis revealed several key fetal heart rate changes predictors after neuraxial labor analgesia. Factors such as the mother’s body mass index (BMI), the duration of the first stage of labor, the use of CSE techniques for analgesia, and the amount of bupivacaine administered played significant roles in predicting fetal heart rate changes. These findings provide crucial practical implications, shedding light on poorly understood medical problems and empowering clinicians to adjust treatment plans accordingly.

Also Read: AI Discovers Antibiotic to Combat Deadly Bacteria

Expanding the Role of AI in Prenatal Care

This groundbreaking study on predicting fetal heart rate changes through ML algorithms is not the only innovative development in the field. Last year, Mayo Clinic researchers developed an AI-based risk prediction model to forecast individual labor risks associated with vaginal delivery. By incorporating patient data, this model helps anticipate potential delivery outcomes for both the mother and the baby. The researchers plan to validate and implement this model within labor units at Mayo Clinic, further revolutionizing prenatal care.

Our Say

Using machine learning tools to flag predictors of fetal heart rate changes represents a significant breakthrough in prenatal care. Researchers have successfully identified key predictors associated with fetal bradycardia following neuraxial analgesia by leveraging ML algorithms. These findings offer invaluable insights into managing and addressing potential risks during labor. As the field of AI continues to expand, we can expect more innovative approaches to enhance prenatal care. Stay tuned for further advancements in this rapidly evolving field.

AIAI in healthcarealgorithmsmachine learningMLnews
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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Breaking Down Social Bias in Artificial Intelligence Algorithms for Cardiovascular Risk Assessment
Breaking Down Social Bias in Artificial Intelligence Algorithms for Cardiovascular Risk Assessment
Machine Learning for Heart Disease Prediction
Machine Learning for Heart Disease Prediction

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