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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

ML Model Predicts Insomnia With Considerable Accuracy

Yana Khare
Yana Khare Last Updated : 14 Apr, 2023
3 min read
Insomnia and other sleep disorders can now be detected using ML models

Imagine a world where predicting a patient’s risk of developing insomnia or other sleep disorders becomes as simple as analyzing their demographic, lifestyle, and health data. Thanks to an innovative medical study, we can now use Machine Learning (ML) models to predict insomnia accurately. This remarkable technology can detect the risk of various sleep disorders, paving the way for more accurate diagnoses and personalized treatment plans.

Also Read: World Bank’s Machine Learning Model to Save Lives in Low-Income Areas

ML Model Predicting Insomnia and Sleep Disorder Risk

A study done by Samuel Y. Huang and Alexander A. Huang can now predict sleeping disorders using machine learning and help d design personalized treatment plans.

A recent study published in the open-access journal PLOS ONE revealed that a machine learning model, XGBoost, could accurately predict a patient’s risk for sleep disorders using demographic and lifestyle data, physical exam results, and laboratory values. The researchers, Samuel Y. Huang from Virginia Commonwealth University School of Medicine and Alexander A. Huang from Northwestern Feinberg University School of Medicine successfully applied the model to analyze publicly available data on 7,929 patients who participated in the US National Health and Nutrition Examination Survey.

Also Read: Bill Gates’ AI Predictions for Health and Education Transformation

Understanding Insomnia and Its Impact on Quality of Life

Insomnia is a prevalent sleep disorder affecting millions of people worldwide. It is characterized by difficulty falling asleep, staying asleep, or waking up too early. Insomnia can significantly impact a person’s quality of life, causing fatigue, irritability, and difficulty concentrating during the day.

Impact of Insomnia and sleep disorders on patients' daily lives.

Traditional Diagnosis vs. Machine Learning

Traditionally, diagnosing insomnia has been a time-consuming and subjective process, with doctors relying on patients to self-report their symptoms. However, machine learning algorithms can predict sleep disorders from patient records with a high degree of accuracy, offering several advantages over traditional methods:

  1. Objectivity: Machine learning relies less on patient self-reporting, enabling doctors to make more accurate diagnoses and develop more effective treatment plans.
  2. Efficiency: Machine learning can analyze vast amounts of data quickly and accurately, reducing the time and cost of diagnosis.

Machine Learning: A Powerful Tool for Sleep Disorder Prediction

Machine learning is a type of artificial intelligence that allows computers to learn from data without being explicitly programmed. In the context of predicting sleep disorders, machine learning algorithms are trained on extensive datasets of patient records. These contain various personal information such as age, sex, medical history, and sleep patterns. Once trained, the algorithm can analyze new patient records and predict sleep disorder likelihood based on the patterns it has learned from the training data. This innovative approach helps doctors make more accurate diagnoses and develop personalized treatment plans for their patients.

Machine learning models to revolutionize the diagnosis and treatment of insomnia.

Methodology: ML Model Analyzing Data for Insomnia & Sleep Disorder Risk

The data analyzed included 684 variables for each of the 7,929 patients. It also encompasses demographic, dietary, exercise, and mental health questionnaire responses and laboratory and physical exam information. The machine learning model identified 64 out of the 684 features as significant (P<0.0001) and used these in the XGBoost model. The model demonstrated an area under the receiver operating characteristic curve (AUROC) of 0.87, with a sensitivity of 0.77 and specificity of 0.77. The top-ranked features by cover included the Patient Health Questionnaire depression survey (PHQ-9), age, physician recommendation of exercise, weight, and waist circumference.

Our Say

Machine learning models have the potential to revolutionize the diagnosis and treatment of sleep disorders, such as insomnia. By analyzing vast amounts of patient data with high accuracy, these models can provide doctors with objective and efficient tools. These tools can be used to predict patients’ risk for sleep disorders. This approach offers several advantages over traditional methods. It also makes it easier for doctors to develop personalized treatment plans that improve their patient’s quality of life. As technology continues to advance, we can expect to see more innovative applications of machine learning in healthcare. These advancements could transform how we diagnose and treat a wide range of medical conditions.

Also Read: Revolutionizing AI in the Life Sciences Industry Using OpenAI’s GPT-3

healthcareInsomniaMachine Learning ModelsTraditional Methods
Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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