Heart Disease Heuristic Prediction With Classification Model

Posted on Jun 7, 2019

Project GitHub | LinkedIn:   Niki   Moritz   Hao-Wei   Matthew   Oren

The skills we demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

A predictive modeling approach using Supervised Learning

MOTIVATION:

In order to accurately classify Heart Disease patients with the presence or absence of the pathological condition, a predictive modeling framework is entailed. Here in this blog post I present a Supervised Machine Learning approach which is developed in order to group patients with and without the disease. The data set pertains to that of "Processed Cleveland Heart Disease" and is made publicly available from the UCI ML repository: (https://archive.ics.uci.edu/ml/datasets/heart+disease).

WORKFLOW & QUESTIONS OF INTEREST:

Exploratory data analysis is first performed on the chosen data set mentioned above, followed by the necessary data preprocessing and data transforms. The final data set is studied using various supervised algorithms. Using K-Fold Cross Validation various algorithms were evaluated to better predict the accuracy of the final model. Overall, the goal of this blog post is to demonstrate the end-to-end approach for a supervised classification model.

METHODOLOGY:

The data set consisted of 302 records of patients with 14 attributes. The attributes comprise of the following variables - (1) Age, (2) Sex, (3) Type of Chest Pain, (4) Resting Blood Pressure, (5) Serum Cholesterol, (6) Fasting Blood Sugar, (7) Resting ECG results, (8) Maximum Heart Rate, (9) Exercise induced Angina, (10) ST Depression, (11) Slope of the exercise peak ST segment, (12) the number of major vessels colored by flouroscopy, (13) Thal, and (14) Diagnosis of the Disease. The following table summarizes the meaning of each variable.

Variable Type Permissible Values
Age Numerical Age in years
Sex Categorical 1=male 0=female
Type of Chest Pain Categorical
1= Typical Angina
2= Atypical Angina
3= Non-Angina Pain
4= Asymptomatic
Resting Blood Pressure Numerical in mm Hg on admission
Serum Cholesterol Numerical in mg/dL
Fasting Blood Sugar Categorical (>120 mg/dL) (1=true 0=false)
Resting ECG Categorical
0 = Normal
1 = ST-T abnormality
2 = Left Ventricle Hypertrophy
Maximum Heart Rate Numerical  
Exercise Induced Angina Categorical 1=yes 0=no
ST Depression Numerical  
Slope from ST Segment Categorical
1 = Up Slope
2 = Flat
3 = Down Slope
Major Vessels colored Numerical 0-3
Thal Categorical
3 = Normal
6 = Fixed Defect
7 = Reversible Defect
Diagnosis of Disease Categorical 1,2,3,4=yes 0=no
  • MISSING DATA & PREPROCESSING: Few records of missing data were noted for the variables of "Major Vessels Color" and "Thal". Since the number of records were less than 5 these values were assigned the respective numerical/categorical variables randomly. Further, since the objective of the model is to classify patients into two groups, the various groups of patients with heart disease (i.e., with response of 1, 2, 3 and 4) are grouped into a single one. 

  • The code can be found in the following link: (https://github.com/uppulury/ClevelandHD)
  • DESCRIPTIVE STATISTICS: The statistics of each feature attribute is studied and figures below depict the distribution for each one of them.

  • Quite clearly the continuous variable have a Gaussian like distribution. Among the categorical variables there is class-imbalance in some variables such as Thal, ST Slope, Resting ECG and Fasting Blood Sugar. Further, the map of correlation among the variables evince strong correlations such as ST Depression and ST Slope.

  • SPOT-CHECK ALGORITHMS: The preprocessed data was split into training and validation sets using Python's train_test_split in the ratio of 80/20. Classification algorithms were applied to the training set using 10-Fold Cross Validation technique to evaluate the model accuracy. Logistic Regression produced the highest accuracy where as SVM produced the lowest accuracy on the data set.

  • In order to improve the accuracy of the model the data set was standardized using Python's Standard Scalar. The same set of classification algorithms were applied and the model accuracy overall improved across all algorithms very likely owing to the underlying Gaussian structure in the variables. The SVM algorithm produced the best fit to the data set with a slightly higher accuracy and low variance compared to the other classification algorithms.

  • FINE-TUNING THE MODEL:

  • (1) LR Fine Tuning - no improvement

  • (2) kNN Fine Tuning - only a very small improvement

  • Overall, the biggest jump in model improvement seems to be for SVM
  • Ensemble Methods were implemented: Gradient Boosting and Extra Trees Classifiers produced the highest accuracy. These accuracy scores are similar to the SVM employing the Standardized data set.

  • Ensemble Methods were Predicted and Scored on the Training and Validation Data Sets: not much improvement is seen here from the 10-fold CV approach.

  • Ensemble Methods were Predicted and Scored on the Standardized Training and Validation Data Sets: not much improvement in accuracy is seen for Ensemble algorithms on standardized data.

  • FINE-TUNE MODEL USING SVM: A GridSearchCV was performed on a range of c_values and various kernel_values using SVM algorithm. The optimal performance was achieved for C=0.1 and a Sigmoid kernel. These parameters were used in the Finalized model to predict and score the algorithm on the Validation Data Set. The accuracy of the final model is 80%.

CONCLUSIONS:

Most algorithms including the Ensemble methods produced high accuracy for the model. The best case accuracy was achieved using an SVM model on the standardized data set. The final results herein presented are for the SVM model and corresponding c_value=0.1 and a 'sigmoid' kernel. Following is the Confusion Matrix from the finalized model:

28 6
6 21

About Author

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI