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Healthcare Support Using Data Mining: A Case Study on Stroke Prediction

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Artificial Intelligence and Machine Learning for Healthcare

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 229))

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Abstract

Data and information have become a valuable asset for small and big organizations in the past few decades. Data is the main ingredient for strategic decision-making, which could give businesses a significant advantage over their competitors, by providing customized services or overall experience to their customers and attracting new ones. For this purpose, data mining techniques are being utilized so that valuable information can be discovered and exploited. There is a vast amount of data generated in the field of healthcare that is not getting fully exploited by traditional methods, for reasons, such as their complexity, velocity, and volume. Therefore, there is a demand for the development of powerful automated data mining tools for the complete utilization of these data, and the uncovering of patterns and precious knowledge about patients, medical claims, treatment costs, hospitals, etc. This work focuses on exploiting the best-known data mining techniques: classification, clustering, and association rule mining, which are utilized extensively in the healthcare industry for incident prediction and general medical knowledge acquisition. The data mining process comprises several steps, such as data selection, pre-processing, transformation, interpretation, and evaluation. The section of the experimentation includes a stroke incidents dataset fetched from the Kaggle dataset provider. This chapter also provides a literature survey on data mining applications in the healthcare sector, while discussing the abovementioned machine learning concepts.

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Abbreviations

Term:

Definition

Bmi:

Body Mass Index

CRISP-DM:

Cross-Industry Standard Process for Data Mining

CRM:

Customer Relationship Management

Data mining algorithm:

A set of heuristics and calculations that creates a model from data

DBSCAN:

Density-Based Spatial Clustering of Applications with Noise

Feature:

An independent variable that can be used as input of a machine learning model

FP-Growth:

Frequent Pattern Growth

GDPR:

General Data Protection Regulation

LEADERS:

Lightweight Epidemiological Advanced Detection Emergency Response System

Machine learning model:

An expression of an algorithm that processes data to explore patterns or make predictions

OLAP:

Online Analytical Processing

Operator:

In RapidMiner, a group of functions that perform actions on input data through parameters and outputs the results of said actions

Parameter:

A special kind of variable used in an algorithm to refer to one of the pieces of data provided as input to it

Process:

In RapidMiner, it is a set of sequentially connected operators represented by a flow design, where each operator provides its output as input to the next one

ROC:

Receiver Operating Characteristic

SEMMA:

Sample, explore, modify, model, assess

SVM:

Support Vector Machine

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Acknowledgements

We would like to express our gratitude to the anonymous reviewers who provided critical feedback during the preparation of this manuscript. Their remarks and recommendations significantly improved the quality of this work.

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Authors and Affiliations

  1. The Data Mining and Analytics Research Group, School of Science and Technology, International Hellenic University, 570 01, Thermi, Greece

    Georgios Michailidis, Michail Vlachos-Giovanopoulos, Paraskevas Koukaras & Christos Tjortjis

Authors
  1. Georgios Michailidis
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  2. Michail Vlachos-Giovanopoulos
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  3. Paraskevas Koukaras
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  4. Christos Tjortjis
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Corresponding author

Correspondence to Christos Tjortjis .

Editor information

Editors and Affiliations

  1. Deakin University, Victoria, VIC, Australia

    Chee Peng Lim

  2. Royal Adelaide Hospital,, Adelaide, SA, Australia

    Ashlesha Vaidya

  3. College of Information Science and Eng, Ritsumeikan University, Kusuatsu, Shiga, Japan

    Yen-Wei Chen

  4. ASF Insignia, Kyndryl Solutions Pvt Ltd,, Gurugram, Haryana, India

    Vaishnavi Jain

  5. Liverpool Hope University, KES International United Kingdom, Shoreham-by-Sea, UK

    Lakhmi C. Jain

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Michailidis, G., Vlachos-Giovanopoulos, M., Koukaras, P., Tjortjis, C. (2023). Healthcare Support Using Data Mining: A Case Study on Stroke Prediction. In: Lim, C.P., Vaidya, A., Chen, YW., Jain, V., Jain, L.C. (eds) Artificial Intelligence and Machine Learning for Healthcare. Intelligent Systems Reference Library, vol 229. Springer, Cham. https://doi.org/10.1007/978-3-031-11170-9_4

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  • DOI: https://doi.org/10.1007/978-3-031-11170-9_4

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