https://archive.ics.uci.edu/ml/datasets/Heart+Disease
Cardiovascular disease or heart disease describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease. From WHO statistics every year 17.9 million dying from heart disease. The medical study says that human life style is the main reason behind this heart problem. Apart from this there are many key factors which warns that the person may/maynot getting chance of heart disease.
From the dataset if we create suitable machine learning technique which classify the heart disease more accurately, it is very helpful to the health organisation as well as patients.
This dataset gives the information realated to heart disease. Dataset contain 13 columns, target is the class variable which is affected by other 12 columns. Here the aim is to classify the target variable to (disease\non disease) using different machine learning algorithm and findout which algorithm suitable for this dataset.
Attribute Information
- Age (age in years)
- Sex (1 = male; 0 = female)
- CP (chest pain type)
- TRESTBPS (resting blood pressure (in mm Hg on admission to the hospital))
- CHOL (serum cholestoral in mg/dl)
- FPS (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
- RESTECH (resting electrocardiographic results)
- THALACH (maximum heart rate achieved)
- EXANG (exercise induced angina (1 = yes; 0 = no))
- OLDPEAK (ST depression induced by exercise relative to rest)
- SLOPE (the slope of the peak exercise ST segment)
- CA (number of major vessels (0-3) colored by flourosopy)
- THAL (3 = normal; 6 = fixed defect; 7 = reversable defect)
- TARGET (1 or 0)
In order to increase the accuracy of the model we use ensembling. Here we use stacking technique.
Stacking or Stacked Generalization is an ensemble machine learning algorithm. It uses a meta-learning algorithm to learn how to best combine the predictions from two or more base machine learning algorithms. The base level often consists of different learning algorithms and therefore stacking ensembles are often heterogeneous.