https://github.com/kmohamedalie/detectecting_heart_disease-svm
Detecting Heart disease in patients using svm
https://github.com/kmohamedalie/detectecting_heart_disease-svm
biology cardiovascular-diseases data-science heart-disease life medicine radial-basis-function support-vector-machines
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Detecting Heart disease in patients using svm
- Host: GitHub
- URL: https://github.com/kmohamedalie/detectecting_heart_disease-svm
- Owner: Kmohamedalie
- License: mit
- Created: 2023-08-02T18:51:54.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2023-08-22T13:55:22.000Z (about 2 years ago)
- Last Synced: 2025-01-02T15:31:59.088Z (10 months ago)
- Topics: biology, cardiovascular-diseases, data-science, heart-disease, life, medicine, radial-basis-function, support-vector-machines
- Language: Jupyter Notebook
- Homepage: https://github.com/Kmohamedalie/Detectecting_Heart_Disease-SVM
- Size: 533 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Detectecting_Heart_Disease-SVM
### **Task:** Detecting Heart disease in patients using SVM
### **Trained Model:** Accuracy: 100%
### **Dataset available on:** [UCI Machine Learning Heart Disease](https://archive.ics.uci.edu/dataset/45/heart+disease) , [Kaggle](https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset)
**Developers' Guide:** [Amazon Machine Learning](https://docs.aws.amazon.com/pdfs/machine-learning/latest/dg/machinelearning-dg.pdf#cross-validation)
**Completed notebook:** [Heart-Disease-SVM](https://github.com/Kmohamedalie/Detectecting_Heart_Disease-SVM/blob/master/Notebook/Heart_Disease_SVM.ipynb)
**Metrics achieved:**
| Algorithm | Precision | Recall | F1-score | Accuracy |
|-----------|-----------|--------|----------|----------|
| Support Vector Machine (SVM) | 100% | 100% | 100% | 100% |
## **Additional Information about the dataset:**
### **Publication Request:**
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
This file describes the contents of the heart-disease directory.
This directory contains 4 databases concerning heart disease diagnosis.
All attributes are numeric-valued. The data was collected from the
four following locations:
1. Cleveland Clinic Foundation (cleveland.data)
2. Hungarian Institute of Cardiology, Budapest (hungarian.data)
3. V.A. Medical Center, Long Beach, CA (long-beach-va.data)
4. University Hospital, Zurich, Switzerland (switzerland.data)
Each database has the same instance format. While the databases have 76
raw attributes, only 14 of them are actually used. Thus I've taken the
liberty of making 2 copies of each database: one with all the attributes
and 1 with the 14 attributes actually used in past experiments.
The authors of the databases have requested:
...that any publications resulting from the use of the data include the
names of the principal investigator responsible for the data collection
at each institution. They would be:
1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation:
Robert Detrano, M.D., Ph.D.
Thanks in advance for abiding by this request.
David Aha
July 22, 1988
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
1. Title: Heart Disease Databases
2. Source Information:
(a) Creators:
-- 1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
-- 2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
-- 3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
-- 4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation:
Robert Detrano, M.D., Ph.D.
(b) Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779
(c) Date: July, 1988
3. Past Usage:
1. Detrano,~R., Janosi,~A., Steinbrunn,~W., Pfisterer,~M., Schmid,~J.,
Sandhu,~S., Guppy,~K., Lee,~S., \& Froelicher,~V. (1989). {\it
International application of a new probability algorithm for the
diagnosis of coronary artery disease.} {\it American Journal of
Cardiology}, {\it 64},304--310.
-- International Probability Analysis
-- Address: Robert Detrano, M.D.
Cardiology 111-C
V.A. Medical Center
5901 E. 7th Street
Long Beach, CA 90028
-- Results in percent accuracy: (for 0.5 probability threshold)
Data Name: CDF CADENZA
-- Hungarian 77 74
Long beach 79 77
Swiss 81 81
-- Approximately a 77% correct classification accuracy with a
logistic-regression-derived discriminant function
2. David W. Aha & Dennis Kibler
--
-- Instance-based prediction of heart-disease presence with the
Cleveland database
-- NTgrowth: 77.0% accuracy
-- C4: 74.8% accuracy
3. John Gennari
-- Gennari, J.~H., Langley, P, \& Fisher, D. (1989). Models of
incremental concept formation. {\it Artificial Intelligence, 40},
11--61.
-- Results:
-- The CLASSIT conceptual clustering system achieved a 78.9% accuracy
on the Cleveland database.
4. Relevant Information:
This database contains 76 attributes, but all published experiments
refer to using a subset of 14 of them. In particular, the Cleveland
database is the only one that has been used by ML researchers to
this date. The "goal" field refers to the presence of heart disease
in the patient. It is integer valued from 0 (no presence) to 4.
Experiments with the Cleveland database have concentrated on simply
attempting to distinguish presence (values 1,2,3,4) from absence (value
0).
The names and social security numbers of the patients were recently
removed from the database, replaced with dummy values.
One file has been "processed", that one containing the Cleveland
database. All four unprocessed files also exist in this directory.
5. Number of Instances:
Database: # of instances:
Cleveland: 303
Hungarian: 294
Switzerland: 123
Long Beach VA: 200
6. Number of Attributes: 76 (including the predicted attribute)
7. Attribute Information:
-- Only 14 used
-- 1. #3 (age)
-- 2. #4 (sex)
-- 3. #9 (cp)
-- 4. #10 (trestbps)
-- 5. #12 (chol)
-- 6. #16 (fbs)
-- 7. #19 (restecg)
-- 8. #32 (thalach)
-- 9. #38 (exang)
-- 10. #40 (oldpeak)
-- 11. #41 (slope)
-- 12. #44 (ca)
-- 13. #51 (thal)
-- 14. #58 (num) (the predicted attribute)
-- Complete attribute documentation:
9. Missing Attribute Values: Several. Distinguished with value -9.0.
10. Class Distribution:
| Database: | 0 | 1 | 2 | 3 | 4 | Total |
| ------------ | --------| --------| --------| --------| --------| ----------|
|Cleveland: | 164 | 55 | 36 | 35 | 13 | 303 |
|Hungarian: | 188 | 37 | 26 | 28 | 15 | 294 |
|Switzerland: | 8 | 48 | 32 | 30 | 5 | 123 |
|Long Beach VA:| 51 | 56 | 41 | 42 | 10 | 200 |