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<!DOCTYPE html>
<html lang="en"><head>
<script src="slides_files/libs/clipboard/clipboard.min.js"></script>
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<meta name="generator" content="quarto-1.3.361">
<meta name="author" content="Elena Boiko, Jacqueline Razo (Advisor: Dr. Cohen)">
<meta name="dcterms.date" content="2025-04-29">
<title>Diagnosing Diseases Using kNN</title>
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<section id="title-slide" data-background-position="right" data-background-size="contain" class="quarto-title-block center">
<h1 class="title">Diagnosing Diseases Using kNN</h1>
<p class="subtitle">An Application of kNN to Diagnose Diabetes</p>
<div class="quarto-title-authors">
<div class="quarto-title-author">
<div class="quarto-title-author-name">
Elena Boiko, Jacqueline Razo (Advisor: Dr. Cohen)
</div>
</div>
</div>
<p class="date">2025-04-29</p>
</section>
<section id="introduction" class="slide level2 center">
<h2>Introduction</h2>
<p>In healthcare, kNN has shown promise in predicting chronic diseases like <strong>diabetes</strong> <span class="citation" data-cites="suriya2023type">(<a href="#/references" role="doc-biblioref" onclick="">Suriya and Muthu 2023</a>)</span> and <strong>hypertension</strong> <span class="citation" data-cites="khateeb2017efficient">(<a href="#/references" role="doc-biblioref" onclick="">Khateeb and Usman 2017</a>)</span>.</p>
<p>In this project, we focus on how kNN can be applied and optimized to predict <strong>diabetes</strong>, a critical and growing public health issue.</p>
<aside class="notes">
<p>kNN is a well-known algorithm that’s already been applied in medical research, including disease prediction for conditions like hypertension and diabetes. In this project, we explored how kNN behaves with health data and how we can optimize it to improve predictive accuracy for diabetes.</p>
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</section>
<section id="why-this-matters" class="slide level2 center">
<h2>Why This Matters</h2>
<p><span class="fragment">- Diabetes affects millions worldwide. Early detection can improve outcomes.</span></p>
<p><span class="fragment">- Machine learning, especially interpretable models like <strong>kNN</strong>, can support diagnosis.</span></p>
<p><span class="fragment">- Our project explores:</span></p>
<p><span class="fragment">- How different <strong>k values</strong>, <strong>distance metrics</strong>, and <strong>preprocessing techniques</strong> affect kNN’s performance.</span></p>
<p><span class="fragment">- Whether kNN is competitive with other models for this task.</span></p>
<aside class="notes">
<p>Diabetes affects over 37 million people in the U.S., and many don’t know they have it. Early detection is critical to avoid complications. Machine learning can help — especially models like kNN that are easy to understand and implement. We tested how different settings, such as the number of neighbors, distance metrics, and preprocessing techniques impact performance. We also compared kNN with models like decision trees and random forests to see how it holds up.</p>
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</section>
<section id="why-we-chose-knn-for-this-project" class="slide level2">
<h2>Why We Chose kNN for This Project</h2>
<p><span class="fragment">Well-suited for medical datasets with small to medium size</span></p>
<p><span class="fragment">Easy to interpret — great for health professionals</span></p>
<p><span class="fragment">Flexible with minimal assumptions</span></p>
<p><span class="fragment">Can impute missing data and detect patterns</span></p>
<aside class="notes">
<p>We chose kNN because it’s simple, interpretable, and works well on structured health data like surveys. Unlike more complex models, kNN doesn’t make strong assumptions — it just compares similar cases. That makes it more transparent, which is valuable in healthcare, where decision-making needs to be explainable.</p>
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</section>
<section id="our-approach" class="slide level2 center">
<h2>Our Approach</h2>
<ul>
<li>Clean and preprocess real-world survey data.</li>
<li>Train kNN models with various configurations.</li>
<li>Evaluate performance and compare with tree-based models.</li>
</ul>
<aside class="notes">
<p>Our project followed a structured approach. We used the CDC’s diabetes health indicators dataset, which includes over 250,000 survey responses. After cleaning and preparing the data, we trained multiple versions of kNN by changing key parameters like the number of neighbors and the distance metric. We then compared the best kNN’s performance with decision trees and random forests to see how it performed in a real-world healthcare prediction task.</p>
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</section>
<section id="method-knn-overview" class="slide level2 center middle">
<h2>Method: kNN Overview</h2>
<p><span class="fragment">k-Nearest Neighbors (kNN) is a <strong>non-parametric, instance-based</strong> learning algorithm</span></p>
<p><span class="fragment">It is a <strong>lazy learner</strong> — no explicit training phase is required</span></p>
<p><span class="fragment">Instead, it classifies new data based on similarity to existing labeled points <span class="citation" data-cites="zhang2016introduction">(<a href="#/references" role="doc-biblioref" onclick="">Zhang 2016</a>)</span></span></p>
</section>
<section class="slide level2">
<h3 id="classification-process"><span class="fragment">Classification Process:</span></h3>
<p><span class="fragment"><strong>1. Distance Calculation:</strong><br>
Measures similarity using metrics like <strong>Euclidean</strong> or <strong>Manhattan</strong> distance</span></p>
<p><span class="fragment"><strong>2. Neighbor Selection:</strong><br>
Hyperparameter <strong>k</strong> defines how many nearby points to consider</span></p>
<p><span class="fragment"><strong>3. Majority Voting:</strong><br>
The most frequent class among the <strong>k nearest neighbors</strong> determines the prediction</span></p>
<aside class="notes">
<p>So, let’s talk about how kNN actually works kNN is known as a lazy learner - it doesn’t train a model in advance. Instead, it stores all the data and makes predictions based on similarity. When a new data point comes in, kNN finds the closest examples from the training set - based on distance - and predicts the most common label among them. This makes it intuitive and highly adaptable, which is why it’s useful in clinical applications where transparency matters.</p>
<p>The process starts by calculating the distance - we used both Euclidean and Manhattan distances in our tests. Then, the model looks at the closest k data points — and this k is something we tune to get better results. Finally, it uses majority voting: whichever class appears most often among the neighbors becomes the prediction.</p>
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</section>
<section id="distance-calculation" class="slide level2 smaller">
<h2>Distance Calculation:</h2>
<p>kNN identifies the nearest neighbors by calculating distances between points.</p>
<div class="fragment">
<p><strong>Euclidean distance:</strong> <span class="citation" data-cites="theerthagiri2022diagnosis">(<a href="#/references" role="doc-biblioref" onclick="">Theerthagiri, Ruby, and Vidya 2022</a>)</span> <span class="math display">\[
d = \sqrt{(X_2 - X_1)^2 + (Y_2 - Y_1)^2}
\]</span></p>
</div>
<div class="fragment">
<p><strong>Manhattan distance:</strong> <span class="citation" data-cites="aggarwal2015data">(<a href="#/references" role="doc-biblioref" onclick="">Aggarwal et al. 2015</a>)</span> <span class="math display">\[
d = |X_2 - X_1| + |Y_2 - Y_1|
\]</span></p>
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</div>
<aside class="notes">
<p>To find “closeness,” kNN uses distance metrics. We tested two types: The most common is Euclidean distance, which measures straight-line distance, and Manhattan distance, which works like a city grid. Since kNN relies on distance, the choice of metric — along with scaling — can significantly affect results. This diagram shows both — we tested both in our project to compare results.</p>
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</section>
<section id="classification-process-1" class="slide level2 center middle">
<h2>Classification Process</h2>
<div class="columns">
<div class="column" style="width:50%;">
<p><span class="fragment">The red square represents a data point to be classified. The algorithm selects the 5 nearest neighbors within the green circle—3 hearts and 2 circles. Based on the majority vote, the red square is classified as a heart.</span></p>
</div><div class="column" style="width:40%;">
<div class="quarto-figure quarto-figure-center">
<figure>
<p><img data-src="images/kNN_picture.png"></p>
<figcaption>Figure 1. kNN with k=5</figcaption>
</figure>
</div>
</div>
</div>
<aside class="notes">
<p>Here’s a simple example of how kNN makes a prediction. The red square is a new point. It looks at the 5 nearest neighbors — in this case, 3 hearts and 2 circles. Since hearts are the majority, the red square is predicted as a heart. This simple majority voting process makes kNN easy to understand and explain - a big advantage in medical settings.</p>
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</section>
<section id="strengths-and-weaknesses-of-knn" class="slide level2 smaller">
<h2>Strengths and Weaknesses of kNN</h2>
<div class="columns">
<div class="column" style="width:50%;">
<h3 id="strengths">Strengths</h3>
<ul>
<li><strong>Simple, intuitive, and non-parametric</strong> — no assumptions about data distribution<br>
</li>
<li><strong>No training phase</strong> — the algorithm learns during prediction<br>
</li>
<li><strong>Performs well</strong> on small to medium datasets, especially when features are well-scaled<br>
</li>
<li><strong>Easy to understand and implement</strong> — ideal for baseline models or educational use</li>
</ul>
</div><div class="column" style="width:50%;">
<h3 id="weaknesses-of-knn">Weaknesses of kNN</h3>
<ul>
<li><strong>Slow prediction time</strong> on large datasets due to distance calculations <span class="citation" data-cites="deng2016efficient">(<a href="#/references" role="doc-biblioref" onclick="">Deng et al. 2016</a>)</span></li>
<li><strong>Sensitive to feature scaling and distance metric choice</strong> <span class="citation" data-cites="uddin2022comparative">(<a href="#/references" role="doc-biblioref" onclick="">Uddin et al. 2022</a>)</span></li>
<li><strong>Choosing the right ‘k’</strong> is critical — too low or high can reduce performance<br>
</li>
<li><strong>Affected by irrelevant or correlated features</strong>, which may distort neighbor similarity</li>
</ul>
</div>
</div>
<aside class="notes">
<p>Here’s a quick summary of what makes kNN useful, and what challenges come with it. It’s simple, doesn’t need training, and works well with smaller datasets when features are properly scaled. That’s why it’s often used for quick testing or in educational settings. But it can struggle with large datasets or noisy features. It’s also sensitive to scaling, and choosing the right k value is really important. That’s why preprocessing and tuning are so important - especially in healthcare, where accuracy and fairness matter.</p>
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</section>
<section id="analysis-and-results" class="slide level2">
<h2>Analysis and Results</h2>
<h3 class="smaller" id="data-source-and-collection">Data Source and Collection</h3>
<p><strong>Data Source:</strong> <a href="https://archive.ics.uci.edu/dataset/891/cdc+diabetes+health+indicators.">CDC Diabetes Health Indicators</a></p>
<p>Collected via the CDC’s Behavioral Risk Factor Surveillance System (BRFSS)</p>
<p>Dataset contains 253,680 survey responses</p>
<p>Covers 21 features: demographics, lifestyle, healthcare, and health history</p>
<p><strong>Target:</strong> Diabetes_binary</p>
<p>(0 = No diabetes, 1 = Diabetes/Prediabetes)</p>
<aside class="notes">
<p>For this project, we used real survey data from the CDC’s BRFSS program. The dataset includes over 250,000 adult responses and covers a wide range of features like age, BMI, physical activity, and general health. Our target was a binary variable indicating diabetes or prediabetes. The dataset’s size and feature variety made it an ideal test case for evaluating how kNN behaves in a real-world health context.</p>
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<section id="data-challenges" class="slide level2 smaller">
<h2>Data Challenges</h2>
<p><strong>Data Quality</strong><br>
<span class="fragment">No missing values</span><br>
<span class="fragment">24,206 duplicate rows detected</span></p>
<p><strong>Outliers & Scaling Sensitivity</strong><br>
<span class="fragment">BMI, MentHlth, PhysHlth had extreme values</span><br>
<span class="fragment">kNN is highly sensitive to scale</span></p>
<p><strong>Feature Relationships</strong><br>
<span class="fragment">No multicollinearity (r < 0.5)</span><br>
<span class="fragment">All features retained for now</span></p>
<p><strong>Early Insight</strong><br>
<span class="fragment">Higher BMI in diabetic cases, but overlapping range</span><br>
<span class="fragment">Used as a predictor along with other features</span></p>
<aside class="notes">
<p>Before modeling, we looked closely at the raw dataset. There were no missing values, but nearly 10% of the data was duplicated - those could bias the model if not removed. BMI and other health features showed outliers, and because kNN uses distance, that’s a problem - large values can dominate. We also checked for correlation but didn’t find any features that were too closely related. One early pattern we noticed: people with diabetes generally had higher BMI, but it wasn’t only factor.</p>
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<section id="class-distribution" class="slide level2 smaller">
<h2>Class Distribution:</h2>
<h3 class="smaller" id="diabetes-class-imbalance">Diabetes Class Imbalance</h3>
<div class="columns">
<div class="column" style="width:50%;">
<p><strong>Key Points</strong><br>
<span class="fragment">Significant class imbalance observed</span><br>
<span class="fragment">Majority class: No Diabetes (0) – <strong>86.07%</strong></span><br>
<span class="fragment">Minority class: Diabetes (1) – <strong>13.93%</strong></span></p>
<p><strong>Impact on Modeling</strong><br>
<span class="fragment">Imbalance can bias predictions</span><br>
<span class="fragment">Models may underpredict diabetes cases</span></p>
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<p><img data-src="slides_files/figure-revealjs/unnamed-chunk-2-1.png" width="864"></p>
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</div>
<aside class="notes">
<p>Another major challenge was class imbalance. About 86% of cases were non-diabetic, and only 14% were diabetic or prediabetic. This imbalance can cause models to favor the majority class and miss early diabetes cases. We addressed this in preprocessing.</p>
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<section id="preparing-the-data" class="slide level2 smaller">
<h2>Preparing the Data</h2>
<ul>
<li><p><strong>Removed 24,206 duplicate rows</strong><br>
<span class="fragment">Diabetic class increased from 13.9% → 15.3%</span></p></li>
<li><p><strong>Kept ordinal features as numeric</strong><br>
<span class="fragment">Age, Education, Income, and GenHlth retained due to natural ordering</span></p></li>
<li><p><strong>Scaled Features with Outliers</strong><br>
<span class="fragment">BMI, MentHlth, PhysHlth scaled with StandardScaler & Robustscaler</span></p></li>
<li><p><strong>Handled class imbalance</strong><br>
<span class="fragment">Applied SMOTE to generate synthetic diabetic samples</span></p></li>
</ul>
<p>➡️ Final dataset: <strong>clean, scaled, and balanced</strong></p>
<aside class="notes">
<p>To get the data ready, we removed duplicates and kept ordinal features like age, education, and income as numeric. The variables BMI, MentHlth, and PhysHlth were standardized using StandardScaler or RobustScaler to ensure equal contribution during distance calculations, a critical aspect for kNN’s accuracy. To fix the class imbalance, we used SMOTE, which creates synthetic diabetic cases to help the model learn both classes equally. After these steps, the dataset was clean, scaled, and balanced — ready for training our kNN models.</p>
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</section>
<section id="knn-model-setup" class="slide level2 smaller">
<h2>kNN Model Setup</h2>
<ul>
<li>Explored different <strong>k values</strong>: 5, 10, 15<br>
</li>
<li>Compared <strong>distance metrics</strong>: Euclidean vs. Manhattan<br>
</li>
<li>Evaluated <strong>weighting methods</strong>: uniform vs. distance<br>
</li>
<li>Tested multiple <strong>scaling techniques</strong><br>
</li>
<li>Included variations with <strong>SMOTE</strong> and <strong>Feature Selection</strong></li>
</ul>
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<thead>
<tr class="header">
<th>Model</th>
<th>k</th>
<th>Distance</th>
<th>Weights</th>
<th>Scaler</th>
<th>SMOTE</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>kNN 1</td>
<td>5</td>
<td>Euclidean (p=2)</td>
<td>Uniform</td>
<td>StandardScaler</td>
<td>No</td>
</tr>
<tr class="even">
<td>kNN 2</td>
<td>15</td>
<td>Manhattan (p=1)</td>
<td>Distance</td>
<td>RobustScaler</td>
<td>No</td>
</tr>
<tr class="odd">
<td>kNN 3</td>
<td>10</td>
<td>Euclidean (p=2)</td>
<td>Uniform</td>
<td>StandardScaler</td>
<td>Yes</td>
</tr>
<tr class="even">
<td>kNN 4</td>
<td>15</td>
<td>Euclidean (p=2)</td>
<td>Distance</td>
<td>StandardScaler</td>
<td>Yes (Feature Selection)</td>
</tr>
</tbody>
</table>
</div>
<aside class="notes">
<p>To better understand kNN’s behavior, we designed four model variations. We changed the number of neighbors, distance type, and weighting method. We also experimented with different scaling techniques and applied SMOTE to handle class imbalance. In one version, we also used feature selection to reduce dimensional noise. The final model - kNN 4 - combined all these enhancements and delivered the strongest performance overall.</p>
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<section id="performance-of-knn-variants" class="slide level2 smaller">
<h2>Performance of kNN Variants</h2>
<h4 class="no-title" id="table-3-performance-comparison-of-knn-models">Table 3: Performance Comparison of kNN Models</h4>
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<th>Model</th>
<th>k</th>
<th>Distance</th>
<th>Weights</th>
<th>Scaler</th>
<th>SMOTE</th>
<th>Accuracy</th>
<th>ROC_AUC</th>
<th>Precision_1</th>
<th>Recall_1</th>
<th>F1_1</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>kNN 1</td>
<td>5</td>
<td>Euclidean (p=2)</td>
<td>Uniform</td>
<td>StandardScaler</td>
<td>No</td>
<td>0.83</td>
<td>0.70</td>
<td>0.41</td>
<td>0.21</td>
<td>0.27</td>
</tr>
<tr class="even">
<td>kNN 2</td>
<td>15</td>
<td>Manhattan (p=1)</td>
<td>Distance</td>
<td>RobustScaler</td>
<td>No</td>
<td>0.84</td>
<td>0.75</td>
<td>0.45</td>
<td>0.16</td>
<td>0.23</td>
</tr>
<tr class="odd">
<td>kNN 3</td>
<td>10</td>
<td>Euclidean (p=2)</td>
<td>Uniform</td>
<td>StandardScaler</td>
<td>Yes</td>
<td>0.69</td>
<td>0.73</td>
<td>0.28</td>
<td>0.64</td>
<td>0.39</td>
</tr>
<tr class="even">
<td>kNN 4</td>
<td>15</td>
<td>Euclidean (p=2)</td>
<td>Distance</td>
<td>StandardScaler</td>
<td>Yes (FS)</td>
<td>0.78</td>
<td>0.88</td>
<td>0.73</td>
<td>0.88</td>
<td>0.80</td>
</tr>
</tbody>
</table>
</div>
<ul>
<li><p><strong>Best configuration: kNN 4</strong><br>
<span class="fragment">k = 15, Euclidean distance, distance weighting</span><br>
<span class="fragment">StandardScaler, SMOTE, and feature selection</span></p></li>
<li><p><strong>Highest Weighted F1 Score: 0.80</strong><br>
<span class="fragment">Achieved recall = 0.88, precision = 0.73</span></p></li>
<li><p>🩺 Most effective at identifying diabetic class (1)</p></li>
</ul>
<aside class="notes">
<p>Table 3 shows the results for each kNN model. As you can see, model performance varies significantly depending on configuration. For instance, KNN 1 and 2 without SMOTE had higher accuracy but poor recall - meaning they missed a lot of diabetic cases. KNN 4, which combined SMOTE and feature selection, offered the best balance - best F1 score of 0.80 and a recall of 0.88 - especially for minority class detection. That’s why we selected kNN 4 as the final model - it was the best at identifying diabetic patients fairly and consistently.</p>
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<section id="comparing-knn-with-tree-models" class="slide level2 smaller">
<h2>Comparing kNN with Tree Models</h2>
<h4 class="no-title" id="table-4-best-knn-vs.-tree-based-models">Table 4: Best kNN vs. Tree-Based Models</h4>
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<th>Model</th>
<th>SMOTE</th>
<th>Accuracy</th>
<th>ROC_AUC</th>
<th>Precision_1</th>
<th>Recall_1</th>
<th>F1_1</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>KNN</td>
<td>Yes</td>
<td>0.78</td>
<td>0.88</td>
<td>0.73</td>
<td>0.88</td>
<td>0.80</td>
</tr>
<tr class="even">
<td>Decision Tree</td>
<td>Yes</td>
<td>0.72</td>
<td>0.80</td>
<td>0.70</td>
<td>0.78</td>
<td>0.74</td>
</tr>
<tr class="odd">
<td>Decision Tree</td>
<td>No</td>
<td>0.86</td>
<td>0.81</td>
<td>0.52</td>
<td>0.15</td>
<td>0.24</td>
</tr>
<tr class="even">
<td>Random Forest</td>
<td>No</td>
<td>0.87</td>
<td>0.82</td>
<td>0.59</td>
<td>0.13</td>
<td>0.21</td>
</tr>
</tbody>
</table>
</div>
<p><span class="fragment">- <strong>kNN achieved the highest F1 score</strong> (0.80) with strong recall on the diabetic class<br>
</span></p>
<p><span class="fragment">- <strong>Decision Tree with SMOTE</strong> performed comparably but slightly lower on F1<br>
</span></p>
<p><span class="fragment">- <strong>Random Forest had highest accuracy</strong>, but <strong>poor recall</strong> (0.13) shows it struggled to detect diabetic cases<br>
</span></p>
<p>Tree-based models offer interpretability, but may need tuning or resampling for minority detection</p>
<aside class="notes">
<p>We also compared the best kNN model to Decision Trees and Random Forests. Random Forest had the highest accuracy — but very poor recall. It missed most diabetic cases. The Decision Tree with SMOTE did better, but it still couldn’t match kNN’s balance of precision and recall. Our tuned kNN outperformed both in detecting the minority class, making it a stronger choice for disease prediction.</p>
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<section id="roc_auc-curves-comparison" class="slide level2 smaller">
<h2>ROC_AUC curves comparison</h2>
<p>This plot compares the ROC curves for all four models.<br>
kNN with Feature Selection performs best (AUC = 0.88), followed by Random Forest.</p>
<img data-src="slides_files/roc_curve.png" class="r-stretch quarto-figure-center"><p class="caption">ROC Curve</p><aside class="notes">
<p>This ROC curve shows how well each model separates diabetic from non-diabetic cases. Our best kNN model, with SMOTE and feature selection, had the highest AUC of 0.88, meaning it balanced true positives and false positives better than the others. Random Forest and Decision Tree performed reasonably well, but neither matched kNN in class separation. This supports the idea that a well-tuned kNN model can be both accurate and clinically effective.</p>
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<section id="conclusion" class="slide level2 smaller">
<h2>Conclusion</h2>
<ul>
<li><p>This project demonstrated that the <strong>k-Nearest Neighbors (kNN)</strong> algorithm can be an effective tool for disease prediction when properly tuned and supported by strong preprocessing.<br>
</p></li>
<li><p>Despite its simplicity, kNN achieved competitive results through careful configuration — including scaling, handling class imbalance, and feature selection.<br>
</p></li>
<li><p>Its interpretability, flexibility, and performance make it a practical choice in healthcare settings, where fairness and transparency are essential.<br>
</p></li>
<li><p>Ultimately, this work highlights how even basic algorithms, when thoughtfully applied, can deliver meaningful insights in real-world medical data.<br>
</p></li>
</ul>
<aside class="notes">
<p>In conclusion, our study shows that kNN is a strong candidate for disease prediction, especially when transparency and recall are priorities. With thoughtful tuning, scaling, and SMOTE, kNN outperformed tree-based models in F1 score and minority class detection. Despite being simple, it handled the diabetes prediction task very well - especially after reducing dimensional noise and balancing the data.</p>
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