Analyzing Heart Disease risk using Key Influencers AI visual in Power BI

The Gartner Magic Quadrant for 2019, announced earlier this month, names Microsoft the leader in Analytics and Business Intelligence Platforms. Microsoft also coincidentally announced the public preview release of its first AI-driven visual for Power BI Key Influencers – this month, among a number of new features for Feb 2019. Inbuilt integration of Power BI with many Azure data products would catapult Power BI miles ahead of Tableau in the long run.

EN-CNTNT-GartnerMQ-BI2019.jpg

Key Influencers is the first of many AI visuals Microsoft would release I assume, in their efforts to democratize AI and make their customers look cool 🙂 In this article, we will go over the various features of this new visual using a publicly available dataset, and get familiar with interpreting the results. Download a copy of Power BI Desktop file for the example I am using in this article and try it out yourself using the free Power BI Desktop tool.

Key Influencers

Key Influencers is a powerful Power BI visual that lets us understand the factors that drive a metric. Power BI analyzes data, ranks the factors that matter, and displays them as key influencers. Under the hood, Power BI uses ML.NET to run logistic regression to calculate the key influencers. Logistic regression is a statistical model that compares different groups to each other, also taking into consideration the number of data points available for a factor.

As the visual is still in preview, there are a number of limitations. My first attempt to use Key Influencers using a survey responses dataset was rather unimpressive.

In my second attempt, I used the popular Heart Disease dataset from UCI to identify key influencers affecting heart disease, and achieved good results.

Heart Disease - Key Influencers Power BI.jpg

Limitations

Before we delve any further, let us take a look at the limitations that apply in the public preview phase of the visual. Pay attention here to avoid frustration as you explore the visual.

Following features are not supported:

  • Analyzing metrics that are aggregates/measures
  • Direct Query / Live Connection / Row Level Security – support
  • Consuming the visual in Power BI Embedded and Power BI mobile apps

Using the Key Influencers Visual

As a first time user, I found the Key Influencers visual intuitive and self-explanatory. It hardly takes a few minutes to set up the visual once you have clean data. Check out Microsoft documentation to understand all aspects of the visual. You could also download a copy of Power BI Desktop file for the example I am using in this article.

Note: Keep column names readable as this will help interpret the visual better

Getting Familiar

There are 2 tabs available within the visual – Key influencers and Top Segments.

The Key influencers tab displays the key factors affecting the metric value selected. In this case, the top factor that affects positive diagnosis of Heart Disease, based on our dataset, is Reversible Defect Thalassemia – increasing the risk of heart disease by 2.83 times when the value of Reversible Defect Thalassemia is 7.

On the right hand side, there is a column chart showing distribution of the selected factor. The check box at the bottom lets you display only influential factor values. We could click-select a different factor to see how it contributes to heart disease.

Heart Disease - Key Influencers Power BI - Getting Familiar.jpg


The Top segments tab displays different segments identified by Power BI within the population, for the metric value selected. Click-select a segment to view more details such as the factor values that define the segment, and how the segment compares against the average. We could also drill down further into the segment to split by additional fields.

Under the hood, Power BI uses ML.NET to run a decision tree to find interesting subgroups. The objective of the decision tree is to end up with a subgroup of datapoints that is relatively high in the metric we are interested in – in our case, the patients who  are suspected to have heart disease.

Heart Disease - Key Influencers Power BI - Top Segment.jpg

 

Heart Disease - Key Influencers Power BI - Top Segment Details.jpg

First Impression

Considering that it is still in preview and is only going to get better, Key Influencers ticks the right boxes. The rationale behind choosing a popular dataset, such as the Heart Disease dataset from UCI, for my example was to allow for comparison of results to Machine Learning models that are already publicly available. Power BI seems to identify influencers correctly and does a good job at presentation. I’m thoroughly impressed by this new feature.

Suggested Reading

If you enjoyed this article, consider reading my other articles on Azure data products.

https://sqlroadie.wordpress.com/2018/04/29/what-is-azure-cosmos-db/
https://sqlroadie.wordpress.com/2018/08/05/azure-cosmos-db-partition-and-throughput/
https://sqlroadie.wordpress.com/2019/02/17/azure-databricks-introduction-free-trial/

Resources:

Download the Power BI workbook used in the example – https://drive.google.com/open?id=13Pt25UPt7dOW3raZmavHHVl7gAStv5uy
Intro to Key Influencers by Microsoft: https://docs.microsoft.com/en-us/power-bi/visuals/power-bi-visualization-influencers
Power BI Feb 2019 feature summary – https://powerbi.microsoft.com/en-us/blog/power-bi-desktop-february-2019-feature-summary/

Heart Disease Data source
Donor:  David W. Aha (aha ‘@’ ics.uci.edu) (714) 856-8779
Creators:

  • Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
  • University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
  • University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
  • V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.

Global AI Bootcamp – Developing AI, responsibly

Global AI Bootcamp, Brisbane 2018

Yesterday, I attended the Global AI Bootcamp Brisbane (at the Precinct, Valley) along with nearly 100 other technology enthusiasts. The event was well organized by David Alzamendi of Wardy IT Solutions and Thiago Passos of SSW Consulting. I rocked up to the event hoping to get an update on the rapidly evolving Data Platform offerings from Microsoft. While the event did meet most of my expectations, it planted one particular seed of thought in my head. As I walked away at end of the day, I was enthralled about the rigorous, almost paranoic, awareness and research of the social responsibility that AI developers and solution providers should exert.

IMG_20181215_093303

Role of Ethics in AI

The event started with playback of the recorded keynote address by distinguished researchers of Microsoft AI. It was probably the small shot of long black coffee I had just had, I sat there wide-eyed and amazed by the wise words of Hanna Wallach, Principal Researcher at Microsoft Research, NYC. Hanna’s research covers a broad range of topics; she was clearly passionate about the impact of AI on society – FATE (Fairness, Accountability, Transparency and Ethics in AI). I had never thought about ethics in AI the same way, but it made perfect sense.

The one reaction that the average Joe has to AI is the notion that it is almost magical, but always reliable and authentic. That’s a dangerous prejudice! AI, much like any other branch of science, can be used for good or bad. The elevated status that AI enjoys amongst the masses, thanks to Hollywood movies, and research companies pitching AI as the field of science that would shape 21st century, leads to the belief that AI = TRUTH! Those in the know, are aware that inherent biases in training data sets lead to biases in scoring. My heart skips a beat just to think how a technologically illiterate person may be led to believe utter lies, much like the predictions of this highly controversial Israeli company – Faception. They claim to be able to apply facial personality analytics technology to predict a person’s IQ, their personality – whether they are an academic researcher or a terrorist, for instance, just by looking at their face.

Utilizing advanced machine learning techniques we developed and continue to evolve an array of classifiers. These classifiers represent a certain persona, with a unique personality type, a collection of personality traits or behaviors. Our algorithms can score an individual according to their fit to these classifiers (sic).

When Vanessa Love, Assistant Director of Integration and DevOps at Australian Bureau of Statistics, talked about Faception during her session – I ain’t afraid of no terminator – at the Bootcamp, my initial impression was that the company was called out on its claims and was obviously identified as a scam. I could resonate with her frustration and anger as she went on to explain how Faception was working with governments, and clients in Fintech and Retail. There are numerous such shocking applications of AI. For instance, Stanford researchers built an AI solution that could predict a person’s sexuality from facial analysis. The only aspect that is more appalling than the intent of their research is the fact that the average Joe doesn’t read the T&Cs – in this case, their model was correct only 81% and 71% of the times in predictions for males and females respectively. So, what about the 48 wrong predictions for every 152 correct predictions? Vanessa also mentioned Amazon’s AI enabled recruiting tool that was stood down due to racial and sexist biases. In this case, AI helped to reveal the truth about inherent historical bias in recruitment practices at one of the biggest technology companies. So, sometimes AI = TRUTH. Tricky? Food for thought!

Will AI enslave human beings?

The age-old question! This is a recurring question I am asked when I discuss AI with less technologically-literate acquaintances. I usually go on to explain how Machine Learning works, and the differences between Supervised and Unsupervised learning. The key point I try to drive home is that AI is not a person or a thing, and more importantly, like all software solutions, it is error prone and not to be taken for granted. When we do take technology for granted, self-driving cars kill people and auto-pilot programs crash planes. Technology is meant to aid and assist, not render humanity obsolete!

Developing AI, responsibly

Luckily, researchers like Hanna Wallach and Yoshua Bengio are actively working on building a code of conduct for AI research and application. A result of that vigil is the Montreal Declaration for Responsible Development of Artificial Intelligence, inked earlier this month. At the time, I read about it and quickly slid that thought to the slow sectors of my brain. I signed the declaration a little while ago. As a technologist, I not only have the responsibility to develop AI responsibly, but also educate others about the pros and cons of  AI solutions.

Other interesting learnings from the Bootcamp

Jernej Kavka, Software Architect at SSW Consulting, presented his experiments with Real-Time Face Recognition using Microsoft Cognitive Services. He explained how his team successfully reduced costs by 99% by applying caching and pre-processing. I found his session remarkable.

Joseph Zhou, Data Scientist and Solution Development Consultant at Avanade – talked about drag-and-drop AI using Azure Machine Learning Services. I found his session crisp and relevant. Later, Yousry Mohamed, Consultant at Readify, explained how to apply DevOps practices in Azure Machine Learning and automating model-selection using “a bit of simple code”. As always, Yousry’s presentation was animated and wonderful.

A day well spent!

Overall, it was a day well spent. Thanks to all sponsors and volunteers for making the event happen! I could tell everyone was excited to be there, and we all went home with various thoughts in our little heads, a little wiser than we were at start of the day. The thought in my head was – what about the Tesla driver who relied on the self-driving capability, what about the black Facebook employee who the soap dispenser denied, what happens when AI goes wrong?

Part 2: Predictive Modeling using R and SQL Server Machine Learning Services

Recap!

Part 1 of Predictive Modeling using R and SQL Server Machine Learning Services covered an overview of Predictive Modeling and the steps involved in building a Predictive Model. Using our sample dataset – Ski Resort rental data – we wanted to predict RentalCount for the year 2015, given the variables – Month, Day, Weekday, Holiday and Snow.

Part 1 covered:
– Getting data from a SQL Server database
– Preparing data for modeling
– Training models
– Comparing results and finalizing a model

We found that for this problem, predictions using the Decision Tree model were more accurate than the Linear Regression model. SQL Server Machine Learning Services (MLS) lets us train and test Predictive Models using R or Python, in the context of SQL Server. Thus, we can build T-SQL programs that contain embedded R/Python scripts that train on data stored in the database.

Deploy Machine Learning code with SQL Server

In this part, we will deploy the R code we wrote in Part 1 to SQL Server. To deploy, we will store the trained model in database and create a stored procedure that predicts using the model. This stored procedure can be invoked from applications.

1. Create Table for storing the model: Here, we create a table in SQL Server to store the trained model. The model will be used for prediction in step 3.

2001_CreateTableRentalRxModels.jpg

2. Create Stored Procedure for generating the model: This stored procedure will use the R scripts we wrote in Part 1 utilizing sp_execute_external_script introduced in SQL Server 2016. To execute sp_execute_external_script, first enable external scripts by using the statement – sp_configure ‘external scripts enabled’, 1;

The function to generate Decision Tree model – rxDTree – is part of the RevoScaleR package for R. RevoScaleR package includes numerous other R functions for importing, transforming, and analyzing data at scale. Point to note is that the functions run on the RevoScaleR interpreter, built on open-source R. It is engineered to leverage the multithreaded and multinode architecture of the host platform, meaning when R code executes within a SQL Server SP, it utilizes parallel processing.

2002_SPToGenerateTrainedDTreeModel

2003_generatetraineddtreemodel.jpg

3. Create Stored Procedure for prediction: Now that we have the model output, we can create an SP that would use the model to predict rental count for new data. Again, we are using the R code covered in Part 1, only that this time we are using it in a SQL Stored Procedure.

2004_SPToPredictRentalCountUsingDTreeModel.jpg

2005_PredictRentalCountForTestDataUsingDTreeModel.jpg

 

2006_PredictRentalCountUsingDTreeModel.jpg

Isn’t that just awesome? We have a Predictive Model that can be used within applications to predict rental count. Now that we have covered a sample project, in Part 3  of the series, I will share my experience using SQL Server Machine Learning Services to solve a problem at my work.

Before we conclude Part 2,

Predict using Native Scoring (SQL Server 2017*): In SQL Server 2017, Microsoft has introduced a native predict function. What this means is we do not need to run R/Python code in a SQL stored procedure to do the actual prediction. Native scoring uses native C++ libraries that reads a trained model stored in binary format (in our case in a SQL Server table), and generate scores for new input data.

2007_CreateTableNativeModelSupport.jpg

2008_GenerateNativeModel.jpg

2010_PredictionUsingNativePredictFunction.jpg

Resources:

Scripts for Part 2: https://drive.google.com/file/d/15fwujRipLg-k2ozOFb9G9PFfBO327zTa/view?usp=sharing
RevoScaleR
https://docs.microsoft.com/en-us/machine-learning-server/r-reference/revoscaler/revoscaler
SQL Server ML Tutorial: https://microsoft.github.io/sql-ml-tutorials/R/rentalprediction/step/3.html
Native Scoring: https://docs.microsoft.com/en-us/sql/advanced-analytics/sql-native-scoring?view=sql-server-2017
RxSerializeModel: https://docs.microsoft.com/en-us/machine-learning-server/r-reference/revoscaler/rxserializemodel
Forecasts and Prediction using SQL Server MLS: https://docs.microsoft.com/en-us/sql/advanced-analytics/r/how-to-do-realtime-scoring?view=sql-server-2017

Part 1: Predictive Modeling using R and SQL Server Machine Learning Services

To R or not to R?

A few months ago, I asked myself an important question – which language to learn first – R or Python? From my research, I found that R is regarded as more old-school and difficult to learn, but Python is more popular. It made perfect sense to learn R first 🙂

For the uninitiated, R is a programming language that makes statistical and mathematical computation easy, and is useful for machine learning/predictive analytics/statistics work.

Along the way, I found the following courses useful.
https://www.edx.org/course/introduction-to-r-for-data-science
https://www.edx.org/course/programming-in-r-for-data-science

The goal has always been to explore Machine Learning Services in SQL Server, and dive deeper thereafter. This 3-part series is a walk through/review of Microsoft’s tutorial on Predictive Modeling using R and SQL Server. The first part deals with preparing data, training a model and using it for prediction.

What is Predictive Modeling?

Predictive Modeling uses statistics to predict outcomes.

In our example, we will use Machine Learning Services for SQL Server 2017 to predict number of rentals for a future date in a ski rental business. This brings up an important question – why do we want to predict? In this case, prediction will help the business be prepared from a stock, staff and facilities perspective. Prediction has numerous, life-changing applications. Read about application of AI in predicting cardiovascular disease by Microsoft for Apollo Hospitals, India.

For the ski rental prediction, we will use test data provided by MS, SQL Server 2017 with Machine Learning Services, and R Studio IDE. Please check the following URL and follow the simple instructions to set up your environment. If you have any question, please use Comments section to drop me a note.
https://microsoft.github.io/sql-ml-tutorials/R/rentalprediction

PredictiveModeling-Steps1

Steps involved in building the predictive model in SQL Server

  1. Getting data
  2. Preparing data
  3. Training models
  4. Comparing results and choosing a model
  5. Deploying the Machine Learning script to SQL Server

1. Getting Data: Okay, let’s get started. In the first step, we will restore sample database – TutorialDB – using the database backup file provided by MS. After restoring the database using SSMS, we will take a look at rental data we will use for training the model.

All scripts used will be provided in the Resources section at bottom of the page.

002_RestoreSampleDatabase-TutorialDB

003_ExamineTrainingData

You will see that we have 453 rows of rental stats. Data is in place, so we are good to move on to Step 2.

2. Preparing Data: Now that database is restored and data available in SQL Server, we will load data to R and transform it. Open R Studio and execute the rental data load script. After loading data, we will examine a few rows/observations and inspect data types. We will then proceed to change types of a few columns to factor.

004_loadrentaldatafromsqlserver.jpg

005_DataPreparation

3. Training Models: Now that data is prepared, we will chose a model that best describes dependency between variables in our dataset. During training, we provide the variables along with the outcome so that our model can train to predict the outcome. Here, we will compare predictions by two different models and choose the more accurate one as our predictive model.
For clarity, let me state that we are trying to predict RentalCount for the year 2015, given the variables – Month, Day, Weekday, Holiday and Snow.

The challenge in Machine Learning is in knowing what various models mean, and when a particular model might be more suitable. MS recommends this cheat sheet as a guide.

006_SplitDatasetToTrainingAndTest

007_TrainingUsingLinearRegressionAndDecisionTreeModels

008_RentalDataPrediction

Comparing results: Here, we compare results to figure out which model predicted more accurately. Decision Tree performed better in this case and we will use the model to deploy our Machine Learning Solution to SQL Server in Part 2

009_RentalDataPlotDifferencePredictedAndActual

Resources:

Scripts for Part 1 (zip file): https://drive.google.com/file/d/1Tzs4qzFXXL-NgxFlKQcuVHKwx90Imr8u/view?usp=sharing
SQL Server R tutorials: https://docs.microsoft.com/en-us/sql/advanced-analytics/tutorials/sql-server-r-tutorials?view=sql-server-2017
Gitghub repo for rental prediction: https://microsoft.github.io/sql-ml-tutorials/R/rentalprediction/
Machine Learning cheat sheet, use with caution 🙂 https://docs.microsoft.com/en-us/azure/machine-learning/studio/algorithm-choice#the-machine-learning-algorithm-cheat-sheet

Azure Cosmos DB Free Trial – walk through of Gremlin API for Graph

After the 2018 Microsoft Ignite event, Microsoft announced a free trial of Azure Cosmos DB. For those who are eager to check out Cosmos DB, this is a great opportunity to familiarize yourself with the hottest No SQL database in the market. Cosmos DB is currently sitting pretty at Rank 29 in the DB Engines Ranking page. Quite an achievement considering that the product is “only 1 year old”.

MS recently went public with the support for Cassandra API. A few years from now, Cosmos DB will be the most popular database offering from Microsoft. If you want to get a gentle intro to Cosmos DB, check out my previous posts – Introduction to Azure Cosmos DB and Azure Cosmos DB – Partition and Throughput.

This article is a walk through of using the Free Trial to get started with Cosmos DB.

Limited Time Free Trial: https://azure.microsoft.com/en-au/try/cosmosdb/

There is no need for a credit card or subscription to avail this free trial. Please do note though that MS is likely to withdraw trial in a few months.

Step 1: Choosing an API/data model
Once you click the above URL, you will be asked to pick an API and data model. Go ahead and pick SQL to check out document (JSON) data model if you are unsure. I want to check out Gremlin API for Graph, so I am choosing Graph in this example.
You will be asked to login with your Microsoft account. If you don’t have one, create a new account.

CosmosDB Trial

Step 2: Choose default options and click on Create container.
Note that container will be created free of cost at a low throughput of 400 RU/s. If you are not familiar with throughput, read my article about Azure Cosmos DB – Partition and Throughput.
You now have a brand new container and the familiar Azure portal access. My trial container has read/write location of Central US.

Azure Portal Database

Closer look: There is a lot going on here, so let us take a closer look.
My container is called Persons and it is in graphdb database. Cosmos DB uses Apache Tinkerpop Gremlin API for graph traversal. Currently, my container Persons is empty, so let us connect a sample application. If you are new to Graph DB, I recommend reading this free ebook from another popular Graph DB product – Neo4j.
https://neo4j.com/graph-databases-book/

Graph DB - Persons

Step 3: Sample project
Click on the Quick start blade to download a sample project and explore graph data.
The sample project already has the connection string set to the trial database, so we can execute the project right away. How awesome is that!

Quickstart

In Step 1, I chose Graph API and model, so my sample project has Gremlin API queries. Read more about Gremlin API here – https://aka.ms/gremlin.

Gremlin API Queries

Step 4: Execute sample project
Now it’s time to execute the project!

Sample Program Execution

Step 5: Data Explorer
Looks like the program has added a number of graph documents to the container. Let us head over to Azure Portal and explore using Data Explorer blade. Click on the Execute Gremlin Query button to take a look at the data added by sample program. Now let us take a closer look at the output.

Graph DB Data Explorer.jpg

Closer look: A quick look shows that the query g.V() returned 4 nodes. Result can be viewed in either JSON or Graph mode.

Output - closer look 1

If we click on the second node – Ben – and zoom in, we get a nice graphical view of ben’s relationships. Click and explore to view related nodes.

Output - closer look 3

Switch to JSON mode to view result in JSON format. Pretty self-explanatory there 🙂

Output - closer look 2

Step 6: Monitor activity
Head over to Activity Log blade to take a look at activity in the Cosmos DB account. This could be downloaded as a .csv or exported to Event Hub for further analysis.

Activity Log

Summary

We took a look at the Cosmos DB Trial offered by Microsoft and got started with Graph API. Graph DB has a number of attractive use cases such as Fraud Detection and Recommendation Engine. Another popular Graph DB is Neo4j. I hope this article helped you understand how powerful Cosmos DB’s multi model support is. If you have any questions, drop me an email or add a comment. I will be happy to help. Add me on LinkedIn to stay connected – https://www.linkedin.com/in/arjunsivadasan/

Further Reading

  1. Introduction to Azure Cosmos DB and Azure Cosmos DB – Partition and Throughput
  2. Intro to Cassandra API in Cosmos DB – https://docs.microsoft.com/en-us/azure/cosmos-db/cassandra-introduction
  3. Intro to Graph DB – https://neo4j.com/graph-databases-book/
  4. Explore Gremlin API – https://aka.ms/gremlin
  5. Cosmos DB playground – https://www.documentdb.com/sql/demo
  6. Microsoft Ignite 2018 Updates – https://news.microsoft.com/uploads/prod/sites/507/2018/09/IGNITEBOOKOFNEWS-5ba95469d658b.pdf