Analyzing the Health Data: An Application of High Utility Itemset Mining

A data science endeavour called "high utility pattern mining" entails finding important patterns based on different factors like profit, frequency, and weight. High utility itemsets are among the various patterns that have undergone thorough study. These itemsets must exceed a minimum thre...

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Bibliographic Details
Published in2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) pp. 153 - 158
Main Authors Padmavathi, Kadium, Saleti, Sumalatha, Tottempudi, Sai Saran
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.11.2023
Subjects
Algorithms
and comparisons
Data science
Itemsets
observations
Prediction algorithms
Profitability
Supply chains
Web services
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Summary:A data science endeavour called "high utility pattern mining" entails finding important patterns based on different factors like profit, frequency, and weight. High utility itemsets are among the various patterns that have undergone thorough study. These itemsets must exceed a minimum threshold specified by the user. This is particularly useful in practical applications like retail marketing and web services, where items have diverse characteristics. High-utility itemset mining facilitates decision- making by uncovering patterns that have a significant impact. Unlike frequent itemset mining, which identifies commonly oc- curring itemsets, high-utility itemsets often include rare items in real-world applications. Considering the application to the medical field, data mining has been employed in various ways. In this context, the primary method involves analyzing a health dataset that spans from 2014 to 2017 in the United States. The dataset includes categories such as diseases, states, and deaths. By examining these categories and mortality rates, we can derive high-utility itemsets that reveal the causes of the most deaths. In conclusion, high-utility pattern mining is a data science activity that concentrates on spotting significant patterns based on objective standards. It has proven valuable in various fields, including the medical domain, where analyzing datasets can uncover high-utility itemsets related to mortality rates and causes of death.Categories and Subject Descriptors - [Health Database Applica- tion] Data Mining
DOI:10.1109/ICAICCIT60255.2023.10466177