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+<div class="sc-cmRAlD dkqmWS"><div class="sc-UEtKG dGqiYy sc-flttKd cguEtd"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">About Dataset</h2></div></div></div><div class="sc-jgvlka jFuPjz"><div class="sc-gzqKSP tNtjD"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-bMmLMY ZURWJ"><hr>
+<h1>Predicting Heart Disease Risk Using Clinical Variables</h1>
+<h3>A 270-Patient Dataset with 13 Features</h3>
+<p>By Robert Hoyt MD <a rel="noreferrer nofollow" aria-label="[source] (opens in a new tab)" target="_blank" href="https://data.world/rhoyt">[source]</a></p>
+<hr>
+<h3>About this dataset</h3>
+
+  <p>The Heart Disease Prediction dataset provides vital insight in the relationship between risk factors and cardiac health. This dataset contains 270 case studies of individuals classified as either having or not having heart disease based on results from cardiac catheterizations - the gold standard in heart health assessment. Each patient is identified by 13 independent predictive variables revealing their age, sex, chest pain type, blood pressure measurements, cholesterol levels, electrocardiogram results, exercise-induced angina symptoms, and the number of vessels seen on fluoroscopy showing narrowing of their coronary arteries. Provided with this data set is an opportunity to evaluate how these characteristics interact with each other in order to determine an individual’s level of risk for developing cardiovascular problems that lead to heart failure or stroke. With this knowledge we can create preventative strategies beyond what traditional medical treatment can do by identifying those at risk earlier and aid our healthcare professionals in treating them better.  By analyzing a combination of clinical variables explained here, we have a powerful tool at our fingertips to try and combat cardiovascular illness before it even has the chance to take root!</p>
+
+<h3>More Datasets</h3>
+
+  <p>For more datasets, click <a aria-label="here (opens in a new tab)" target="_blank" href="https://www.kaggle.com/thedevastator/datasets">here</a>.</p>
+
+<h3>Featured Notebooks</h3>
+
+  <ul>
+  <li>🚨 <strong>Your notebook can be here!</strong> 🚨! </li>
+  </ul>
+
+<h3>How to use the dataset</h3>
+
+  <p>This dataset provides the information necessary to predict whether a patient has heart disease or not, using clinical variables. The dataset contains information from 270 patients and 13 independent predictive variables.</p>
+  <p>To use this dataset effectively, it is best to start by understanding the column attributes and their importance in predicting heart disease risk. </p>
+  <p>The attributes are Age, Sex, Chest Pain Type, BP (blood pressure), Cholesterol level, FBS over 120 (fasting blood sugar), EKG Results (electrocardiogram results), Max HR (maximum heart rate), Exercise Angina status, ST Depression ( depression of ST segment on ECG) , Slope of ST(slope of the ST segment on the ECG), Number of Vessels Fluroscopy (number of vessels seen on fluoroscopy) and Thallium Stress test results. </p>
+  <p>Understanding how each variable relates to heart disease risk will help you make better predictions based on this data set. For example age is one variable that affects the odds of someone having a heart attack or stroke as it relates to arterial blockages- so it is important to note if age could be an independent factor or if other factors could be enhancing the odds in an individual patient’s case . It should also be noted that although cholesterol levels are included in this data set , other laboratory parameters such as hdl cholesterol , triglycerides and ldl need to also be considered when assessing overall cardiovascular risk . Knowing gender can also play an important role when analyzing possible trends for diagnosing new cases with suspected cardiac symptoms . Finally , understanding exercise angina status can provide critical information about a patient’s history with exercise -induced chest pain which could result from myocardial ischemia . </p>
+  <p>Once you have understood all these factors along with other pertinent medical history including family medical background and lifestyle habits like smoking/ vaping , consuming alcohol etc., , you can use machine learning techniques along with logistic regression models such as k-nearest neighbours algorithms &amp; decision trees to predict whether someone has a higher risk for developing cardiovascular problems like coronary artery disease [CAD]. This can help physicians plan out preventive measures for reducing chances for future myocardial infarctions [MIs] among such patients </p>
+
+<h3>Research Ideas</h3>
+
+  <ul>
+  <li>Developing predictive models to predict a patient's risk of having heart disease based on demographic and medical data.</li>
+  <li>Creating an algorithm to classify patients with and without heart disease using all the available clinical characteristics including age, sex, chest pain type, BP, cholesterol, FBS over 120 and EKG results.</li>
+  <li>Identifying patterns in the data that could help identify which factors have the biggest influence on a person's risk for developing heart disease. This could lead to better preventive health care measures for heart disease in general or for specific groups of people at higher risk than others (e.g., due to certain age or gender)</li>
+  </ul>
+
+<h3>Acknowledgements</h3>
+
+  <p>If you use this dataset in your research, please credit the original authors.<br>
+  <a rel="noreferrer nofollow" aria-label="Data Source (opens in a new tab)" target="_blank" href="https://data.world/rhoyt">Data Source</a></p>
+
+<h3>License</h3>
+
+  <p><strong>License: <a rel="noreferrer nofollow" aria-label="Dataset copyright by authors (opens in a new tab)" target="_blank" href="https://creativecommons.org/licenses/by/4.0/">Dataset copyright by authors</a></strong></p>
+  <ul>
+  <li>You are free to:<ul>
+  <li><strong>Share</strong> - copy and redistribute the material in any medium or format for any purpose, even commercially.</li>
+  <li><strong>Adapt</strong> - remix, transform, and build upon the material for any purpose, even commercially.</li></ul></li>
+  <li>You must:<ul>
+  <li><strong>Give appropriate credit</strong> - Provide a link to the license, and indicate if changes were made.</li>
+  <li><strong>ShareAlike</strong> - You must distribute your contributions under the same license as the original.</li>
+  <li><strong>Keep intact</strong> - all notices that refer to this license, including copyright notices.</li></ul></li>
+  </ul>
+
+<h3>Columns</h3>
+<p><strong>File: Heart_Disease_Prediction.csv</strong></p>
+<table>
+<thead>
+<tr>
+<th>Column name</th>
+<th>Description</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td><strong>Age</strong></td>
+<td>The age of the patient. (Numeric)</td>
+</tr>
+<tr>
+<td><strong>Sex</strong></td>
+<td>The gender of the patient. (Categorical)</td>
+</tr>
+<tr>
+<td><strong>Chest pain type</strong></td>
+<td>The type of chest pain experienced by the patient. (Categorical)</td>
+</tr>
+<tr>
+<td><strong>BP</strong></td>
+<td>The blood pressure level of the patient. (Numeric)</td>
+</tr>
+<tr>
+<td><strong>Cholesterol</strong></td>
+<td>The cholesterol level of the patient. (Numeric)</td>
+</tr>
+<tr>
+<td><strong>FBS over 120</strong></td>
+<td>The fasting blood sugar test results over 120 mg/dl. (Numeric)</td>
+</tr>
+<tr>
+<td><strong>EKG results</strong></td>
+<td>The electrocardiogram results of the patient. (Categorical)</td>
+</tr>
+<tr>
+<td><strong>Max HR</strong></td>
+<td>The maximum heart rate levels achieved during exercise testing. (Numeric)</td>
+</tr>
+<tr>
+<td><strong>Exercise angina</strong></td>
+<td>The angina experienced during exercise testing. (Categorical)</td>
+</tr>
+<tr>
+<td><strong>ST depression</strong></td>
+<td>The ST depression on an Electrocardiogram. (Numeric)</td>
+</tr>
+<tr>
+<td><strong>Slope of ST</strong></td>
+<td>The slope of ST segment electrocardiogram readings. (Categorical)</td>
+</tr>
+<tr>
+<td><strong>Number of vessels fluro</strong></td>
+<td>The amount vessels seen in Fluoroscopy images. (Numeric)</td>
+</tr>
+<tr>
+<td><strong>Thallium</strong></td>
+<td>The Thallium Stress test findings. (Categorical)</td>
+</tr>
+<tr>
+<td><strong>Heart Disease</strong></td>
+<td>Whether or not the patient has been diagnosed with Heart Disease. (Categorical)</td>
+</tr>
+</tbody>
+</table>
+<h3>Acknowledgements</h3>
+
+  <p>If you use this dataset in your research, please credit the original authors.<br>
+  If you use this dataset in your research, please credit <a rel="noreferrer nofollow" aria-label="Robert Hoyt MD (opens in a new tab)" target="_blank" href="https://data.world/rhoyt">Robert Hoyt MD</a>.</p>
+</div></div></div>
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