heart

This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The "goal" field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4.

datasets

Implementing some of the pillars of an automated machine learning pipeline such as (i) Automated data preparation, (ii) Feature engineering, (iii) Model building in classification context that includes techniques such as (a) Regularised regression [1], (b) Logistic regression [2], (c) Random Forest [3], (d) Decision tree [4] and (e) Extreme Gradient Boosting (xgboost) [5], and finally, (iv) Model explanation (using lift chart and partial dependency plots). Accomplishes the above tasks by running the function instead of writing lengthy R codes. Also provides some additional features such as generating missing at random (MAR) variables and automated exploratory data analysis. Moreover, function exports the model results with the required plots in an HTML vignette report format that follows the best practices of the industry and the academia. [1] Gonzales G B and De Saeger (2018) <doi:10.1038/s41598-018-21851-7>, [2] Sperandei S (2014) <doi:10.11613/BM.2014.003>, [3] Breiman L (2001) <doi:10.1023/A:1010933404324>, [4] Kingsford C and Salzberg S (2008) <doi:10.1038/nbt0908-1011>, [5] Chen Tianqi and Guestrin Carlos (2016) <doi:10.1145/2939672.2939785>.

Dayanand Ubrangala

DriveML

Self-Drive Machine Learning Projects

Dayanand Ubrangala 

Sayan Putatunda

Kiran R

Ravi Prasad Kondapalli

heart function

A data frame with 303 rows and 14 variables:<dl>
 <dt><code>age</code></dt>
<dd>integer Age</dd> <dt><code>sex</code></dt>
<dd>integer Sex</dd> <dt><code>cp</code></dt>
<dd>integer chest pain type (4 values)</dd> <dt><code>trestbps</code></dt>
<dd>integer resting blood pressure</dd> <dt><code>chol</code></dt>
<dd>integer serum cholestoral in mg/dl</dd> <dt><code>fbs</code></dt>
<dd>integer fasting blood sugar &gt; 120 mg/dl</dd> <dt><code>restecg</code></dt>
<dd>integer resting electrocardiographic results (values 0,1,2)</dd> <dt><code>thalach</code></dt>
<dd>integer maximum heart rate achieved</dd> <dt><code>exang</code></dt>
<dd>integer exercise induced angina</dd> <dt><code>oldpeak</code></dt>
<dd>double oldpeak = ST depression induced by exercise relative to rest</dd> <dt><code>slope</code></dt>
<dd>integer the slope of the peak exercise ST segment</dd> <dt><code>ca</code></dt>
<dd>integer number of major vessels (0-3) colored by flourosopy</dd> <dt><code>thal</code></dt>
<dd>integer thal: 3 = normal; 6 = fixed defect; 7 = reversable defect</dd> <dt><code>target_var</code></dt>
<dd>integer the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4</dd>
</dl>

Format

Dataset Heart Disease - Classifications — heart

A data frame with 303 rows and 14 variables:<dl>
 <dt><code>age</code></dt>
<dd>integer Age</dd>

 <dt><code>sex</code></dt>
<dd>integer Sex</dd>

 <dt><code>cp</code></dt>
<dd>integer chest pain type (4 values)</dd>

 <dt><code>trestbps</code></dt>
<dd>integer resting blood pressure</dd>

 <dt><code>chol</code></dt>
<dd>integer serum cholestoral in mg/dl</dd>

 <dt><code>fbs</code></dt>
<dd>integer fasting blood sugar &gt; 120 mg/dl</dd>

 <dt><code>restecg</code></dt>
<dd>integer resting electrocardiographic results (values 0,1,2)</dd>

 <dt><code>thalach</code></dt>
<dd>integer maximum heart rate achieved</dd>

 <dt><code>exang</code></dt>
<dd>integer exercise induced angina</dd>

 <dt><code>oldpeak</code></dt>
<dd>double oldpeak = ST depression induced by exercise relative to rest</dd>

 <dt><code>slope</code></dt>
<dd>integer the slope of the peak exercise ST segment</dd>

 <dt><code>ca</code></dt>
<dd>integer number of major vessels (0-3) colored by flourosopy</dd>

 <dt><code>thal</code></dt>
<dd>integer thal: 3 = normal; 6 = fixed defect; 7 = reversable defect</dd>

 <dt><code>target_var</code></dt>
<dd>integer the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4</dd>


</dl>

heart: Dataset Heart Disease - Classifications

Description

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Examples