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Accuracy improvement of classification model becomes main research objective in various fields. Selecting important features and removing outliers of a dataset are two effective solutions for improving model accuracy. Information Gain is one of the feature selection methods that can be considered as a solution for selecting important features of a dataset. Information Gain selects the variable that maximizes the information gain, which in turn minimizes the entropy and best splits the dataset into groups for effective classification. Aside of selecting important feature, removing outlier is also necessary for improving accuracy of the classification model. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the powerful outlier removal methods which can identify with significant accuracy the clusters of random shape and size in large databases corrupted with noise. Therefore, in this study, we propose the accuracy improvement of heart disease classification model using Information Gain and DBSCAN applied to various machine learning algorithms. One publicly available heart disease dataset (Cleveland) is utilized in this study to build the classification model. The results showed that after implementing Information Gain, the accuracy of the model applied to Gaussian Naïve Bayes, Logistic Regression, Multi-Layer Perceptron, Support Vector Machine, Decision Tree, Random Forest, and Extreme Gradient Boosting algorithms increases as much as 1.31% in average. The accuracy also increases when DBSCAN is applied to the model after utilizing Information Gain, with the number of improvements is around 0.62%.