--- title: I Built a Heart Disease Predictor With 9 ML Models.... description: A hands-on walkthrough of building a heart disease severity predictor using the UCI Heart Disease dataset and nine ML classifiers. The author covers the full... canonical: https://daily.dev/posts/i-built-a-heart-disease-predictor-with-9-ml-models-here-s-which-one-actually-won--3lil9ojhw twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: I Built a Heart Disease Predictor With 9 ML Models. Here’s Which One Actually Won. | daily.dev og:description: A hands-on walkthrough of building a heart disease severity predictor using the UCI Heart Disease dataset and nine ML classifiers. The author covers the full... og:url: https://daily.dev/posts/i-built-a-heart-disease-predictor-with-9-ml-models-here-s-which-one-actually-won--3lil9ojhw og:image: https://api.daily.dev/og/posts/3lIL9OjHw.png og:image:alt: I Built a Heart Disease Predictor With 9 ML Models. Here’s Which One Actually Won. og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # I Built a Heart Disease Predictor With 9 ML Models. Here’s Which One Actually Won. **[Medium](https://daily.dev/sources/medium_js)** · 8 min read · 0 upvotes · 0 comments ## Summary A hands-on walkthrough of building a heart disease severity predictor using the UCI Heart Disease dataset and nine ML classifiers. The author covers the full pipeline: EDA, handling heavy missing data (up to 66% in some columns) with custom RandomForest-based imputation, outlier removal, and a 5-fold cross-validated comparison of models including XGBoost, Random Forest, SVM, and KNN. XGBoost won with 61.6% test accuracy on a 5-class severity problem — not the 90%+ seen in tutorials that quietly collapse it to binary classification. The post honestly reports imputation quality metrics, flags the dataset's gender imbalance, and explains why lower accuracy on a harder multiclass problem is more credible than inflated binary results. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why does my model trained on the UCI heart disease dataset only get around 60% accuracy instead of the 90%+ I see in other tutorials? Most tutorials quietly collapse the UCI heart disease target into binary classification (disease vs. no disease), which is an easier task than predicting the original five ordered severity classes (0 through 4). When using the full five-class target with 920 rows, nine classifiers tested with 5-fold cross-validation topped out at 61.6% test accuracy with XGBoost, followed by Random Forest at 60.9%, making that range more realistic than inflated binary results. _daily.dev surfaces practical writeups like this for developers benchmarking classifiers on the same imbalanced dataset._ ### How do you impute missing values for columns like ca and thal that are missing in more than half the rows? Rather than dropping rows or filling with mean/mode, train a RandomForestClassifier (for categorical columns) or RandomForestRegressor (for continuous columns) on the rows where the target column is known, using all other columns as features, then predict the missing values. In this dataset, thal imputation reached 73.56% accuracy and fbs reached 78.92%, while trestbps had a weak R² of only 0.06. _developers dealing with heavily missing medical or tabular data can find similar imputation approaches on daily.dev._ ### Which machine learning models perform worst on mixed categorical and continuous imputed medical data? Distance-based methods struggled the most: SVM scored 47.8% and KNN scored 45.7% test accuracy on the UCI heart disease multiclass target, the two lowest of nine classifiers tested. This tracks with their sensitivity to mixed categorical and continuous feature spaces containing imputed values, compared to tree-based methods like XGBoost (61.6%) and Random Forest (60.9%) that handled it better. _daily.dev helps developers comparing classifier trade-offs on messy real-world feature spaces stay informed._ ## Similar posts on daily.dev - [What Training Five Machine Learning Models On The Same Dataset Taught Me About Machine Learning.](https://daily.dev/posts/what-training-five-machine-learning-models-on-the-same-dataset-taught-me-about-machine-learning--atcb6m9em) · Medium · 2 upvotes · 0 comments - [Can AI help predict which heart-failure patients will worsen within a year?](https://daily.dev/posts/can-ai-help-predict-which-heart-failure-patients-will-worsen-within-a-year--0ifhzgvtm) · MIT News · 0 upvotes · 0 comments - [I Trained Six Models for Fraud Detection, and the Best One Isn't in Production](https://daily.dev/posts/i-trained-six-models-for-fraud-detection-and-the-best-one-isn-t-in-production-owj2jt66q) · Towards Data Science · 0 upvotes · 0 comments - [FAU Researchers Use Quantum Machine Learning to Predict Heart Disease](https://daily.dev/posts/fau-researchers-use-quantum-machine-learning-to-predict-heart-disease-vjtz1qlns) · The Quantum Insider · 0 upvotes · 0 comments - [DIY \#22 - Build a Churn Detection Model from Scratch](https://daily.dev/posts/diy-22---build-a-churn-detection-model-from-scratch-lsntlkfqx) · Machine Learning Pills · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#scikit](https://daily.dev/tags/scikit), [#xgboost](https://daily.dev/tags/xgboost) [View this post on daily.dev](https://daily.dev/posts/i-built-a-heart-disease-predictor-with-9-ml-models-here-s-which-one-actually-won--3lil9ojhw) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"I Built a Heart Disease Predictor With 9 ML Models. 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