The dataset used in this project is the Cleveland Heart Disease dataset taken from the UCI repository. The dataset consists of 303 individual's data. In the actual dataset we had 76 features but for our study we chose only 14 important ones. They are:
- Age
- Sex
- Chest-pain type
- Resting Blood Pressure
- Serum Cholesterol
- Fasting Blood Sugar
- Resting ECG
- Max heart rate achieved
- Exercise induced angina
- ST depression induced by exercise relative to rest
- Peak exercise ST segment
- Number of major vessels (0–3) colored by fluoroscopy
- Thal
- Diagnosis of heart disease URL: https://archive.ics.uci.edu/ml/datasets/Heart+Disease
METHODS AND ALGORITHMS USED Naïve Bayes Naive Bayes is a simple but an effective classification technique which is based on the Bayes Theorem. It assumes independence among predictors, i.e., the attributes or features should be not correlated to one another or should not, in anyway, be related to each other. Even if there is dependency, still all these features or attributes independently contribute to the probability and that is why it is called Naïve.
Support Vector Machine Support Vector Machine is an extremely popular supervised machine learning technique(having a predefined target variable) which can be used as a classifier as well as predictor. For classification, it finds a hyper-plane in the feature space that differentiates between the classes. An SVM model represents the training data points as points in the feature space, mapped in such a way that points belonging to separate classes are segregated by a margin as wide as possible. The test data points are then mapped into that same space and are classified based on which side of the margin they fall.
Decision Tree Decision trees are supervised learning algorithms. This technique is mostly used in classification problems. It performs effortlessly with continuous and categorical attributes. This algorithm divides the population into two or more similar sets based on the most significant predictors. Decision Tree algorithm, first calculates the entropy of each and every attribute. Then the dataset is split with the help of the variables or predictors with maximum information gain or minimum entropy. These two steps are performed recursively with the remaining attributes.
Random Forest Random Forest is also a popularly supervised machine learning algorithm. This technique can be used for both regression and classification tasks but generally performs better in classification tasks. As the name suggests, Random Forest technique considers multiple decision trees before giving an output. So, it is basically an ensemble of decision trees. This technique is based on the belief that more trees would converge to the right decision. For classification, it uses a voting system and then decides the class whereas in regression it takes the meaning of all the outputs of each of the decision trees. It works well with large datasets with high dimensionality.