| WUSS |
2026 |
Building an AI-Powered Smart Car Recommendation System Using Python |
Leon Rod Davoody,
Chary Akmyradov |
source |
13 |
| WUSS |
2026 |
From Prediction to Explanation: Per-Patient SHAP Attribution for a Random Forest Diabetes Risk Classifier |
Leon Rod Davoody,
Chary Akmyradov |
source |
12 |
| PharmaSUG |
2026 |
From Exploratory Data Analysis to Machine Learning: Continuing My Python Journey |
Leon Rod Davoody |
source |
13 |
| PharmaSUG |
2026 |
Machine Learning Models for Predicting Diabetes Using the PIMA Indians Dataset |
Leon Rod Davoody |
source |
11 |
| PHUSE US |
2026 |
AI-Driven Quality Risk Management for Contract Manufacturing Oversight: An ML Approach |
Kiran Kumar Gande |
source
presentation source |
7 |
| PHUSE US |
2026 |
Enhancing Clinical Trial Dataset Mapping with AI/ML Techniques: An Approach for CDISC Variable Prediction |
Parvee Kumar |
source
presentation source |
7 |
| PHUSE APAC |
2026 |
Machine Learning Algorithms to Explore the Relationship Between Primary Obsessive-Compulsive Disorder (OCD) and Co-morbid Depression Symptoms |
Ajay Kumar Singh,
Sabyasachi Mukherjee |
source
presentation source |
12 |
| SESUG |
2025 |
A Generational Analysis of Mental Health Trends and Risk Factors Using CDC BRFSS Data |
Shubham Choudhary,
Amit Navare |
|
14 |
| PHUSE US |
2025 |
Applications of Machine Learning and Artificial Intelligence in Real-World Data in Personalised Medicine for Non-Small Cell Lung Cancer Patients |
Samiul Haque |
source
presentation source |
8 |
| PHUSE US |
2025 |
Python's Future in Clinical Trials: Innovations and Collaborative Advancements |
Shweta Shukla,
Sohan Lal |
source
presentation source |
10 |
| MWSUG |
2024 |
Charting Your Organization's Machine Learning Roadmap |
Ryan Paul Lafler |
source |
7 |
| MWSUG |
2024 |
Introduction to Machine Learning in SAS [presentation] |
Melodie Rush |
source |
71 |
| MWSUG |
2024 |
Predicting the Impact of Storms on Utility Customers [presentation] |
David J. Corliss |
source |
23 |
| PHUSE EU |
2024 |
Leveraging 'Ensemble' Machine Learning Models to Drive Automation of Specification Generation and Data Mapping in Clinical Trial Data Submission |
Venu Mallarapu |
source
presentation source |
10 |
| SESUG |
2024 |
Charting Your Organization's Machine Learning Roadmap |
Ryan Paul Lafler |
source |
7 |
| SESUG |
2024 |
Comparative Analysis of Sentiment Prediction Methods Using Aspects in Consumer Reviews |
Harish Palakurthi |
source |
8 |
| SESUG |
2024 |
Unlocking Insights: Harnessing Supervised Machine Learning for Advanced Data Analysis |
Vindhya Hegde |
source |
23 |
| WUSS |
2024 |
Charting Your Organization's Machine Learning Roadmap |
Ryan Paul Lafler |
source |
6 |
| WUSS |
2024 |
Flight Delay Analysis and Prediction |
Eric C Braga,
Miguel Ángel Bravo Martínez del Valle,
Riley Rutan,
Fernanda Carrillo |
source |
19 |
| WUSS |
2024 |
From Keys to Credit: A Deep Dive into Homeownership's Influence on Loan Quality |
Yi-Che Chen,
Debanik Chakraborty |
source |
15 |
| PharmaSUG |
2024 |
Applications of Machine Learning and Artificial Intelligence in Real World Data in Personalized Medicine for Non-Small Cell Lung Cancer Patients |
Sherrine Eid,
Samiul Haque,
S. Robert Collins |
source |
8 |
| PharmaSUG |
2024 |
Bayesian Additive Regression Trees for Counterfactual Prediction and Estimation |
Michael Lamm |
source |
10 |
| PharmaSUG |
2024 |
MLNR or Machine Learning in R |
Chuck Kincaid |
source |
23 |
| PHUSE US |
2024 |
Case study of verification and validation methods for algorithmic predictive biomarkers |
Kartik Shetty |
source
presentation source |
4 |
| PHUSE US |
2024 |
Risk-Based Monitoring Using AI/ML Detection of Systematic Bias in Clinical Data |
Kostiantyn Drach,
Sergey Glushakov |
source
presentation source |
12 |
| WUSS |
2023 |
Introduction to Machine Learning - Descriptions and Best Practices |
Jim Box |
source |
13 |
| PharmaSUG |
2023 |
What is Machine Learning, anyway? |
Jim Box |
source |
11 |
| SESUG |
2022 |
To Lend, or Not to Lend, That Is the Question! Evaluating Loan Application with Machine Learning |
Rafae Abdullah,
Sucharitha Vallabhaneni,
Jaideep Muley |
source |
17 |
| PharmaSUG |
2022 |
Generating Real World Evidence On The Likelihood Of Metastatic Cancer In Patients Through Machine Learning In Observational Research: Insights For Prevention |
Sherrine Eid,
Samiul Haque,
S. Robert Collins |
source |
8 |
| PharmaSUG |
2022 |
Potential features analysis affect Covid-19 spread |
Yida Bao,
Philippe Gaillard |
source |
11 |
| PHUSE EU |
2021 |
HoW: RWD Applications in Pharma with a Focus on Clinical Trials [presentation] |
Berber Snoeijer,
Samiul Haque |
source |
47 |
| PharmaSUG |
2021 |
Random Forest Machine Learning Application using R Shiny for Project Timeline Forecasting |
Hrideep Antony,
Aman Bahl |
source |
8 |
| SGF |
2021 |
Investing in Education Intelligently |
Sam Edison,
Rushya Puttam,
Akhil Emani,
Nithisha Katta |
source
presentation |
7 |
| SGF |
2021 |
Next-Generation Data Mining and Machine Learning: Python and SAS Together at Last |
George S. Habek,
Suman Kumar Das |
source
presentation |
24 |
| SGF |
2021 |
Reliability of Individual Predictions Estimation for Claim Risk-Scoring Models |
Yuriy Chechulin,
Jina Qu,
Hudson Mahboubi,
Terrance D'souza |
source |
9 |
| SESUG |
2020 |
Application and comparison with several types of Ensemble Algorithms |
Yida Bao,
Philippe Gaillard |
source |
6 |
| SESUG |
2020 |
Model Comparison of Machine Learning Techniques to detect signs of Penetrating Abdominal and Pelvic Injuries using Base JMP and R Integration |
Melvin T. Alexander |
source |
25 |
| SGF |
2020 |
Analyzing Non-normal Data: Application to Missing Data Problems |
Niloofar Ramezani |
source |
10 |
| SGF |
2020 |
Developing Credit Scores with Telco Data Using Machine Learning and Agile Methodology |
Luciano Diettrich |
source |
14 |
| SGF |
2020 |
Explanatory Machine Learning Model Plots for Epidemiological and Real-World Evidence Studies |
David Olaleye |
source |
22 |
| SGF |
2020 |
How to Explain Your Black-Box Models in SAS Viya |
Funda Güneş |
source |
26 |
| SGF |
2020 |
Relationship analysis and customer churn forecasting in a financial cooperative institution |
Carolina Silva,
Brunno Sousa Ramos |
source |
7 |
| SGF |
2020 |
The Seven Most Popular Machine Learning Algorithms for Online Fraud Detection and Their Use in SAS |
Patrick Maher |
source |
6 |
| SGF |
2020 |
Variable Selection Using Random Forests in SAS |
Denis Nyongesa |
source |
11 |
| PharmaSUG |
2020 |
How to let Machine Learn Clinical Data Review as it can Support Reshaping the Future of Clinical Data Cleaning Process |
Mirai Kikawa,
Yuichi Nakajima |
source |
9 |
| PHUSE US |
2020 |
A Comparison of Machine Learning Algorithms for Binary Classification of Free-text Fields Using SAS, R, and Python |
Michael S Rimler |
source
presentation source |
12 |
| PHUSE US |
2020 |
Is Artificial Intelligence our destiny? Data-driven Decision-making in a Dynamic Landscape using SAS Viya |
Aman Bahl,
Hrideep Antony |
source
presentation source |
13 |
| PHUSE US |
2020 |
Machine Learning for CDISC Specification Recommendations |
S. Preston Burns |
source
presentation source |
7 |
| PHUSE US |
2020 |
Using RWD in the Form of EHR Data to Predict Adverse Events and Outcomes |
Corey Young,
Susan Olson |
source |
4 |
| SESUG |
2019 |
A Random Forest Example of the Boston Housing Data using the Base SAS and the PROC_R macro in SAS Enterprise Guide |
Melvin T. Alexander |
source |
18 |
| SESUG |
2019 |
Advanced Statistical Modeling within Machine Learning and Big Data Analytics |
Niloofar Ramezani,
George Mason |
source |
16 |
| SESUG |
2019 |
Summarizing some conventional methods to classify a binary target |
Yida Bao,
Philippe Gaillard |
source |
10 |
| SCSUG |
2019 |
Pattern Analysis and Predictive Policing Using Socio-Economic Factors |
Trishla Mishra |
source |
12 |
| WUSS |
2019 |
Variable Selection Using Random Forests in SAS |
Denis Nyongesa |
source |
11 |
| PharmaSUG |
2019 |
Performing Analytics on Free-Text Data Fields: A Programer-s Wurst Nitemare |
Michael S Rimler,
Matt Pitlyk |
source |
18 |
| SGF |
2019 |
A Comparison of Algorithmic and Epidemiologic Approaches for Modeling Predictors of Type II Diabetes |
Audrey Whittle |
source |
8 |
| SGF |
2019 |
From Words to Actions: Using Text Analytics to Drive Business Decisions |
Reid Baughman |
source |
7 |
| SGF |
2019 |
Generating Synthetic Instruments for Stress Testing: Machine Learning and Econometrics in SAS Viya |
Rocco Cannizzaro,
Christian Macaro |
source |
8 |
| SGF |
2019 |
Machine Learning and Predictive Analytics in SAS Enterprise Miner and SAS/STAT Software |
David Richard Cutler |
source |
21 |
| SGF |
2019 |
Playing Favorites: Our Top 10 Model Studio Features in SAS Visual Data Mining and Machine Learning |
Wendy Czika,
Peter Gerakios |
source |
16 |
| PHUSE US |
2019 |
Integrative Analysis of Circulating Tumor Cell Counts and Gene Expression Levels |
Yauheniya Cherkas |
source
presentation source |
8 |
| SCSUG |
2018 |
Detecting Fake News |
Gaurav Jhanwar |
source |
15 |
| PHUSE EU |
2018 |
How Machine Learning Can Automate Data Standardization in Clinical Trials [presentation] |
Fanyi Zhang |
source |
16 |
| SESUG |
2018 |
Predicting the Risk of Attrition for Undergraduate Students using SAS Enterprise Miner |
Sivateja Reddy Kandula |
source |
12 |
| MWSUG |
2018 |
Pan-Cancer Epigenetic Biomarker Selection from Blood Sample Using SAS |
Xi Chen,
Jin Xie,
Qingcong Yuan |
|
9 |
| WUSS |
2018 |
Analyzing international assessments: An ensemble and model comparison approach |
Chong Ho Yu,
Hyun Seo Lee,
Siyan Gan,
Emily Lara |
source |
13 |
| SGF |
2018 |
A Cautionary Tale of Using Machine Learning within Credit Risk in the Utilities Industry |
Spiros Potamitis,
Paul Malley |
source |
8 |
| SGF |
2018 |
Claim Risk Scoring Using Survival Analysis Framework and Machine Learning with Random Forest |
Yuriy Chechulin |
source |
15 |
| SGF |
2018 |
Do You Need to Put Your Data on a Diet? |
Howard Plemmons Jr.,
Priya Sharma |
source |
7 |
| SGF |
2018 |
Improving Game Strategies with Data Analytics: The Case of Euroleague Professional Basketball [presentation] |
Raha Akhavan-Tabatabaei,
Cem Yigman |
source |
35 |
| SGF |
2018 |
Interpreting Black-Box Machine Learning Models Using Partial Dependence and Individual Conditional Expectation Plots |
Ray Wright |
source |
19 |
| SGF |
2018 |
Mitigating the Effects of Class Imbalance Using SMOTE and Tomek Link Undersampling in SAS |
Kyle Biron |
source |
20 |
| SGF |
2018 |
Prediction and Interpretation for Machine Learning Regression Methods |
David Richard Cutler |
source |
16 |
| MWSUG |
2017 |
Untangle Customer's Incrementality using Uplift Modeling with a Case Study on Direct Marketing Campaign |
Hairong Gu,
Yi Cao,
Chao Xu |
|
8 |
| WUSS |
2017 |
Exploratory Analysis of the Factors Related to Gun Mortality |
Gabrielle Ilenstine,
Stephanie Mendoza,
Evelyn Fox |
source |
17 |
| SGF |
2017 |
N-Stage Machine Learning Analysis with the LUA Procedure Helps Solve Big Data Analysis Problems |
Ryo Kiguchi,
Eri Sakai,
Yoshitake Kitanishi,
Akio Tsuji |
source |
8 |
| SGF |
2017 |
Predictive Accuracy: A Misleading Performance Measure for Highly Imbalanced Data |
Josephine Akosa |
source |
12 |
| SGF |
2017 |
Random Forests with Approximate Bayesian Model Averaging |
Tiny Du Toit,
Andre De Waal |
source |
11 |
| SGF |
2017 |
SAS Masters: Exploratory Analysis of the Factors Related to Gun Mortality |
Stephanie Mendoza,
Gabrielle Ilenstine |
source |
23 |
| SGF |
2017 |
Using Python with SAS Cloud Analytic Services |
Kevin D. Smith,
Xiangxiang Meng |
source |
23 |
| SGF |
2017 |
Using SAS to Analyze Emergency Department Visits: Medicaid Patients Compared to Other Pay Sources |
Bradley Casselman,
Taylor K. Larkin,
Denise J. McManus |
source |
16 |
| SGF |
2017 |
What Statisticians Should Know about Machine Learning |
David Richard Cutler |
source |
13 |
| SCSUG |
2016 |
Determining the functionality of Water Pumps in Tanzania Using SAS EM and VA |
Indra Kiran Chowdavarapu,
Vivek Damodaran Manikandan |
source |
9 |
| SESUG |
2016 |
Using JMP to apply Decision Trees and Random Forests as Screening Tools for Limiting Candidate Predictors in Regression Models |
Jason Brinkley |
source |
10 |
| MWSUG |
2016 |
An Introduction to the HPFOREST Procedure and its Options |
Carl Nord,
Jacob Keeley |
|
12 |
| MWSUG |
2016 |
Identifying the factors responsible for loan defaults and classification of customers using SAS Enterprise Miner |
Juhi Bhargava,
Prashanth Reddy Musuku |
|
13 |
| WUSS |
2016 |
Social Media, Anonymity, and Fraud: HP Forest Node in SAS Enterprise Miner |
Taylor K. Larkin,
Denise J. McManus |
source |
5 |
| SGF |
2016 |
Application of Data Mining Techniques in Improving Breast Cancer Diagnosis |
Josephine Akosa |
source
presentation |
10 |
| SGF |
2016 |
Diagnosing Obstructive Sleep Apnea: Using Predictive Analytics Based on Wavelet Analysis in SAS/IML Software and Spectral Analysis in PROC SPECTRA |
Woranat Wongdhamma |
source |
12 |
| SGF |
2016 |
Forecasting Short Time Series Using SAS Forecast Server |
Cobey Abramowski,
Christian Haxholdt,
Chris Houck,
Benjamin Perryman |
source |
5 |
| SGF |
2016 |
Mass-Scale, Automated Machine Learning and Model Deployment Using SAS Factory Miner and SAS Decision Manager |
Jonathan Wexler,
Steve Sparano |
source |
19 |
| SGF |
2015 |
The More Trees, the Better! Scaling Up Performance Using Random Forest in SAS Enterprise Miner |
Narmada Deve Panneerselvam |
source |
10 |
| SGF |
2014 |
An Overview of Machine Learning with SAS Enterprise Miner |
Patrick Hall,
Jared Dean,
Ilknur Kaynar Kabul,
Jorge Silva |
source
zip |
24 |
| SGF |
2014 |
What's New in SAS Enterprise Miner 13.1 |
Jared Dean,
Jonathan Wexler |
source |
16 |
| SGF |
2013 |
A Data-Driven Analytic Strategy for Increasing Yield and Retention at Western Kentucky University Using SAS Enterprise BI and SAS Enterprise Miner |
Matt T. Bogard |
source |
12 |
| NESUG |
2011 |
How to Increase Sales of Orthopedic Equipment in United States: Factor and Cluster Analysis Using SAS and R |
George Obsekov |
|
15 |
| PHUSE EU |
2008 |
Statistically guided review of safety data in clinical trials |
Michael O'Connell,
Harry Southworth |
source |
12 |
| MWSUG |
2008 |
Chemical Informatics using JMP PowerMV and ChemModLab |
S. Stanley Young,
Thomas H. Burger,
Michael S. Lajiness,
Wolf-D. Ihlenfeldt |
|
40 |
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