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<!DOCTYPE html>
<html lang="en">
<head>
<title>Predicting Incidence of Alzheimer's Disease from ADNI dataset</title>
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<body>
<nav>
<a href="/alzheimers-prediction/">Home</a>
| <a href="/alzheimers-prediction/Report/Report.html">Report</a>
| <a href="/alzheimers-prediction/EDA/EDA.html">EDA</a>
| <a href="/alzheimers-prediction/model.html">Model</a>
</nav>
<h1>Predicting Incidence of Alzheimer's Disease from ADNI dataset</h1>
<p>Jonathan Fisher, Kezi Cheng, Nikhil Mallareddy</p>
<p>The aim of our analysis is to identify the minimal set of predictors that can lower the overall cost of
Alzheimer's diagnosis without sacrificing a substantial degree of diagnostic accuracy. Reading about Alzheimer's
is pretty despiriting and data science can be hard. So, we present the following cartoon (courtesy <a href="https://www.xkcd.com/">xkcd</a>)
to lighten up the atmosphere.</p>
<img src = "cartoon.png" alt = "img courtesy: xkcd" style="float:left;width:600px;height:600px;">
</body>
</html>
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<blockquote>
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<p>Here is <em>emph</em> and <strong>bold</strong>.</p>
<p>Here is some inline math $\alpha = \frac{\beta}{\gamma}$ and, of-course, E rules:</p>
<script type="math/tex; mode=display">G_{\mu\nu} + \Lambda g_{\mu\nu} = 8 \pi T_{\mu\nu} .</script> ->