Apriori Algorithm Source Code In C11/10/2022 We check the benefits of using the Apriori algorithm by comparing the classification accuracies of certain ML algorithms when all attributes are considered as features in the dataset and when only the discovered attributes via Apriori are considered as features. Then, using these frequently occurring diagnosis and procedure codes as present/absent features, along with other features, we apply certain supervised ML algorithms. Using the Apriori algorithm, we generate frequently appearing diagnosis and procedure codes in a healthcare dataset. The Apriori algorithm works on the fundamental property that an item-set is frequent only if all its non-empty subsets are also frequent. Specifically, we take a healthcare dataset involving consumption of two pain medications in the US, and we apply different ML algorithms both with and without a prior feature-discovery process involving the Apriori algorithm. The primary goal of this article is to highlight the potential of Apriori frequent item-set mining algorithm for feature discovery before application of different ML algorithms. One way to address the challenge posed by data sets with several thousands of features is by using frequent item-set mining algorithms(e.g., Apriori algorithm) to discover a subset of features because these algorithms look at the associations among items while selecting frequent item-sets. Similarly, in ANOVA, researchers need to test assumptions of normalityand independence, which may not be the case when features depend upon each other. Another disadvantage of the PCA method is that it is an elimination technique that considers a single feature to be important or unimportant to the problem rather than a group of features being important. Although both PCA and ANOVA approach seem to help in feature discovery, these approaches may become computationally expensive to apply in problems where there are thousands of features in data (e.g., thousands of diagnostic and procedure codes across several patient cases in medical datasets). In ANOVA, the features that describe the most substantial proportion of the variance are the features that are retained in data. ANOVA is a collection of statistical models used to analyze the differences between group means and their associated procedures (such as "variation" between different groups). PCA is a linear feature-based approach that uses eigenvector analysis to determine critical variables in a high dimensional data without much loss of information. Popular algorithms like Principal Component Analysis (PCA for features reduction) and Analysis of variance (ANOVA for features selection) have been used for datasets with a large number of features in the past. Feature reduction technique reduces the number of attributes by creating new combinations of attributes whereas, feature selection techniques include and exclude attributes present in the data without changing them. Two techniques have been suggested in the literature to address the problem of datasets possessing a large number of features: feature reduction (dimensionality reduction) and feature selection. Presence of thousands of features in the data is problematic for classification algorithms as processing these features require large memory usage and high computational costs. The presence of many attributes in data sets may make it difficult to discover the most relevant features for predicting outcomes via ML algorithms. In general, healthcare data sets are large, and they may contain several thousands of features to enable learning of patterns in data. To predict healthcare outcomes accurately, ML algorithms need to focus on discovering appropriate featuresfrom data. Mining hidden patterns in healthcare data sets could help healthcare providers and pharmaceutical companiesto plan quality healthcare for patients in need. In fact, the existence of electronic health records (EHRs) has allowed researchers to apply ML algorithms to learn hidden patterns in data to improve patient outcomes like the type of medications patients consume and the frequency at which they consume these medications. In recent years, ML algorithms have also been utilized in the healthcare sector. Pickett 2,e, and Varun Dutt 1,fġApplied Cognitive Science Laboratory, Indian Institute of Technology Mandi, Himachal Pradesh, India – 175005Ī and the early ‘90s, machine-learning (ML) algorithms have been used to help mine patterns in data sets concerning fraud detection and others. Shruti Kaushik 1,a, Abhinav Choudhury 1,b, Nataraj Dasgupta 2,c, Sayee Natarajan 2,d, Larry A.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |