codelight2711 / heart-attack-prediction

In this kernel, I have tried to analyze the Heart Disease dataset. The idea is to better understand the relationship of various factors in the dataset that can be related to the heart disease. The original database contains 76 attributes, but all published experiments refer to using a subset of 14 of them,so we will be using these 14 features for analysing.

Heart-Attack-Prediction

In this kernel, I have tried to analyze the Heart Disease dataset. The idea is to better understand the relationship of various factors in the dataset that can be related to the heart disease. The original database contains 76 attributes, but all published experiments refer to using a subset of 14 of them,so we will be using these 14 features for analysing. This intermediate level data set provides health examination data among 303 patients who presented with chest pain and might have been suffering from heart disease. The data set has 14 attributes including patients' age, gender, blood pressure, cholesterol level, and their heart disease status indicating whether the diagnosed patient were found to have a heart disease or not. This data set is recommended for learning and practicing your skills in classification modelling techniques. Feel free to do exploratory data analysis and data visualization on the data set. The Following data dictionary gives more details on this data set:

The column names are respectively :

Column Position Attribute Name Description Attribute Type

#1 age age of the patient quantitative

#2 sex gender of the patient qualitative

#3 cp type of chest pain (1:'Typical Angina', 2:'atypical angina', 3:'non-anginal pain', 4:'asymptomatic') qualitative

#4 trestbps resting blood pressure (in mm Hg on admission to the hospital) quantitative

#5 chol serum cholesterol in mg/dl quantitative

#6 fbs (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) qualitative

#7 restecg resting ECG results (0: 'normal', 1 and 2: 'abnormal') qualitative

#8 thalach maximum heart rate achieved quantitative

#9 exang exercise induced angina (1 = yes; 0 = no) qualitative

#10 oldpeak ST depression induced by exercise relative to rest quantitative

#11 slope the slope of the peak exercise ST segment (1: 'upsloping', 2: 'flat', 3: 'down sloping) qualitative

#12 ca number of major vessels (0-3) colored by fluoroscopy qualitative

#13 thal thalassemia (3: 'normal', 6: 'fixed defect' , 7: 'reversible defect') qualitative

#14 num angiographic disease status (0: no heart disease, > 0: heart disease) qualitative

Acknowledgement

This data set has been sourced from the Machine Learning Repository of University of California, Irvine Heart Disease Data Set (UC Irvine). The UCI page mentions following as the principal investigator responsible for the data collection:

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.