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Mining Data to Deal with Epidemics: Case Studies to Demonstrate Real World AI Applications

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Handbook of Artificial Intelligence in Healthcare

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 211))

Abstract

The massive growth of Big Data kickstarted a new era for data analytics and knowledge discovery. Data mining algorithms are employed to analyze different types of data, which reside in complex information networks. Researchers focus on producing usable knowledge by taking advantage of opportunities in various domains (e.g., healthcare, social media, energy etc.). Epidemics and disease outbreaks raised concerns about effective infectious disease management in communities around the world. Therefore, they encourage the use of AI methods for management and prevention, in order to mitigate disease spread, and contain outbreaks. This work engages in predictive analytics, utilizing classification, as well as descriptive analytics utilizing association rule mining and clustering, which are widely used in healthcare and medicine, either for predicting outbreaks or for extracting usable information from healthcare and medical data. Certain steps need to be considered when attempting to perform data analysis, such as data extraction, cleaning, preprocessing, transformation, interpretation and evaluation. The experimental part of this chapter integrates widely used datasets retrieved from the UCI Machine Learning Repository related with the healthcare domain. This chapter offers a literature review on data mining in epidemics, while thoroughly discussing all the aforementioned concepts. It also presents a complete process/cycle of the required steps to analyze data retrieved from healthcare and medical sources. Hence, the research questions addressed can be summarized to the following: Q1. Which are the pervasive types of analytics involving the domains of medicine and healthcare? Q2. How is data mining performed in the fields of healthcare and medicine? Q3. Which are the widespread techniques and methods utilized? These questions are discussed and elaborated, through a concise, informative and educational narration.

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Abbreviations

Term :

Definition

API (Application Programming Interface):

A set of functions and protocols that enables the data transmission and communication between software applications.

Data mining algorithms:

Mathematical and computational expressions of patterns found in datasets.

Data point:

An observation derived from a set of one or more measurements, presented either numerically or graphically.

Feature:

An attribute or variable of a dataset that can be used for analysis.

Instance:

A subset of the overall dataset or a single row of data.

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Author information

Authors and Affiliations

  1. The Data Mining and Analytics Research Group, School of Science and Technology, International Hellenic University, 57001, Thessaloniki, Greece

    Christina Nousi, Paraskevi Belogianni, Paraskevas Koukaras & Christos Tjortjis

Authors
  1. Christina Nousi
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  2. Paraskevi Belogianni
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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. Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC, Australia

    Chee-Peng Lim

  2. Royal Adelaide Hospital, Adelaide, SA, Australia

    Ashlesha Vaidya

  3. St. Joseph’s Hospital and Medical Center, Stockton, CA, USA

    Kiran Jain

  4. Avanti Institute of Cardiology, Nagpur, Maharashtra, India

    Virag U. Mahorkar

  5. KES International, Shoreham-by-Sea, UK

    Lakhmi C. Jain

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Nousi, C., Belogianni, P., Koukaras, P., Tjortjis, C. (2022). Mining Data to Deal with Epidemics: Case Studies to Demonstrate Real World AI Applications. In: Lim, CP., Vaidya, A., Jain, K., Mahorkar, V.U., Jain, L.C. (eds) Handbook of Artificial Intelligence in Healthcare. Intelligent Systems Reference Library, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-79161-2_12

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