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https://github.com/ziraddingulumjanly/unsupervised-learning-implementation-on-heartattackdataset
This study aims to identify distinct subgroups within a dataset of patients with heart attack-related features using unsupervised learning techniques: k-means and Hierarchical
https://github.com/ziraddingulumjanly/unsupervised-learning-implementation-on-heartattackdataset
dataset heartattack kaggle kmeans-algorithm kmeans-clustering pca-analysis tsne-algorithm unsupervised-machine-learning
Last synced: 9 days ago
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This study aims to identify distinct subgroups within a dataset of patients with heart attack-related features using unsupervised learning techniques: k-means and Hierarchical
- Host: GitHub
- URL: https://github.com/ziraddingulumjanly/unsupervised-learning-implementation-on-heartattackdataset
- Owner: ziraddingulumjanly
- License: mit
- Created: 2024-11-29T08:38:03.000Z (23 days ago)
- Default Branch: main
- Last Pushed: 2024-11-29T11:05:18.000Z (23 days ago)
- Last Synced: 2024-11-29T11:26:17.477Z (23 days ago)
- Topics: dataset, heartattack, kaggle, kmeans-algorithm, kmeans-clustering, pca-analysis, tsne-algorithm, unsupervised-machine-learning
- Language: Python
- Homepage: https://www.kaggle.com/code/nourhankarm/heart-attack-detection/input?select=Heart+Attack.csv
- Size: 1.41 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Implementation of k-means and Hierarchical clustering methods to the Heart Attack Dataset
The identification and grouping of individuals based on these health indicators are crucial for understanding patterns that may correlate with heart attack risk. By partitioning the data into meaningful groups without relying on explicit outcome labels, the analysis highlights trends in health indicators and provides insights into natural groupings of health profiles. These findings can facilitate targeted interventions, improve risk stratification, and enhance understanding of patient heterogeneity, potentially guiding medical decision-making and identifying high-risk subpopulations for further study.
# ZG2024