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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 Tool Developed to Detect Parkinson’s Disease Years Before Symptoms

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 15 May, 2023
3 min read

Parkinson’s disease, a degenerative neurological disorder that affects millions of people worldwide, may soon be detectable years before the onset of symptoms. Researchers from UNSW Sydney, in collaboration with Boston University, have developed an innovative AI tool that can do this. It utilizes machine learning to analyze metabolites in the body, potentially revealing early markers of Parkinson’s disease. This breakthrough could revolutionize early detection and pave the way for more effective treatment strategies.

Also Read: Use of ML in HealthCare: Predictive Analytics and Diagnosis

CRANK-MS, an AI tool developed by UNSW researchers & Boston University, can detect Parkinson's disease years before symptoms appear.

Unveiling the Promise of Early Parkinson’s Disease Detection

The study conducted by scientists from the UNSW School of Chemistry focused on blood samples taken from healthy individuals who were part of the Spanish European Prospective Investigation into Cancer and Nutrition (EPIC). By analyzing the metabolites present in these samples, the researchers aimed to identify unique combinations that could act as potential indicators or warning signs for Parkinson’s disease. Remarkably, the tool developed by UNSW researchers, called CRANK-MS (Classification and Ranking Analysis using Neural network generates Knowledge from Mass Spectrometry), demonstrated great promise in accurately predicting the disease’s presence up to 15 years in advance.

Also Read: ML Model Predicts Insomnia With Considerable Accuracy

Uncovering Metabolite Associations Through Machine Learning

Traditional methods of analyzing metabolomics data typically rely on statistical approaches that examine correlations between specific molecules. However, CRANK-MS takes a more comprehensive approach, considering the associations between multiple metabolites using machine learning algorithms. The tool harnesses the computational power required to analyze hundreds to thousands of metabolites simultaneously. This provides more insight and a deeper understanding of the disease. By integrating all available information without data reduction, CRANK-MS allows researchers to identify metabolites that may have been missed using conventional approaches.

The ML algorithms used in the AI tool examine and analyze metabolites to predict the chance of diseases.

Potential for Early Intervention and Improved Diagnosis

Parkinson’s disease is currently diagnosed by observing physical symptoms such as hand tremors, leaving no room for early intervention. However, atypical symptoms like sleep disorders and apathy can manifest decades before motor symptoms appear. This is where CRANK-MS could prove invaluable, enabling early detection & the opportunity for preventive measures to mitigate the disease’s progression. With up to 96% accuracy in predicting Parkinson’s disease, CRANK-MS holds immense potential as a non-invasive and efficient diagnostic tool. By utilizing the tool at the first sign of atypical symptoms, physicians may be able to rule in or rule out the future risk of developing Parkinson’s disease.

Also Read: Revolutionary AI Tool Developed to Diagnose Childhood Blindness

CRANK-MS: A Powerful Tool for Disease Diagnosis

CRANK-MS, the AI tool developed by UNSW researchers, is not limited to Parkinson’s disease detection alone. It serves as a versatile platform for diagnosing various diseases by utilizing metabolomics data and machine learning algorithms. The user-friendly nature of CRANK-MS allows researchers to generate results in less than 10 minutes using a conventional laptop. This accessibility opens up possibilities for applying the tool to other diseases, identifying new biomarkers, and improving the overall diagnostic and monitoring capabilities in the field of medicine.

CRANK-MS helps in detecting Parkinson's as well as many other diseases years before symptoms appear.

Implications for Future Research and Collaboration

The groundbreaking work carried out by the UNSW Sydney researchers and their collaborators at Boston University marks a significant milestone in Parkinson’s disease research. The development of CRANK-MS and its potential to detect the disease years before symptoms manifest offers hope for early intervention and improved patient outcomes. However, the road ahead involves extensive validation studies and global collaboration to ensure the tool’s accuracy and reliability across diverse populations.

Also Read: Machine Learning & AI for Healthcare in 2023

Our Say

With the advent of the CRANK-MS tool, the landscape of Parkinson’s disease detection will see a paradigm shift. The ability to predict the disease years before symptoms appear provides an unprecedented opportunity for early intervention. This leads to improved patient outcomes and a deeper understanding of the disease’s underlying mechanisms. As researchers continue to refine and validate the AI tool, the day may come when detecting Parkinson’s disease becomes as routine as a simple blood test, ushering in a new era of proactive healthcare and transformative treatments.

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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