Medical Data Mining for Heart Diseases and the Future of Sequential Mining in Medical Field

Rjeily, Carine Bou; Badr, Georges; Hassani, Amir Hajjarm El; Andrès, Emmanuel

doi:10.1007/978-3-319-94030-4_4

Cited by 47 publications

(33 citation statements)

References 65 publications

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“…After calculating the probability of each class, the high probability class do assign for the complete transaction [7]. Naïve Bayes is a common approach used to predict classes for different types of dataset such as educational data mining [32] and medical data mining [18]. This model also useful for classifying different kind of dataset like sentiment analysis [33] and virus detection [34].…”

Section: B Naïve Bayesmentioning

confidence: 99%

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Implementation of Machine Learning Model to Predict Heart Failure Disease

Alotaibi¹

2019

IJACSA

175

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In the current era, Heart Failure (HF) is one of the common diseases that can lead to dangerous situation. Every year almost 26 million of patients are affecting with this kind of disease. From the heart consultant and surgeon's point of view, it is complex to predict the heart failure on right time. Fortunately, classification and predicting models are there, which can aid the medical field and can illustrates how to use the medical data in an efficient way. This paper aims to improve the HF prediction accuracy using UCI heart disease dataset. For this, multiple machine learning approaches used to understand the data and predict the HF chances in a medical database. Furthermore, the results and comparative study showed that, the current work improved the previous accuracy score in predicting heart disease. The integration of the machine learning model presented in this study with medical information systems would be useful to predict the HF or any other disease using the live data collected from patients.

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Section: B Naïve Bayesmentioning

confidence: 99%

“…It measures the probability of A given that B as shown in following equation. Then working on finding out the distinct class for each attributes, in this scenario all other variables are not dependent on each other [18]. Naïve Bayes uses the following equation for measuring the probability: [34] www.ijacsa.thesai.org…”

Section: B Naïve Bayesmentioning

confidence: 99%

Implementation of Machine Learning Model to Predict Heart Failure Disease

Alotaibi¹

2019

IJACSA

175

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“…In the setting of chronic diseases, as in chronic heart disease or in diabetes, several informatics solutions or tools have been developed and used, such as artificial neural network (ANN) algorithms, data mining software, and ontology [45,46]. In this context of AI, three clinical datasets are of particular interest: (1) patients' phenotype; (2) patients' electronic medical records containing physicians' notes, laboratory test results, as well as other information on diseases, treatments, and epidemiology that may be of interest for association studies and predictive modeling on prognosis and drug responses; and (3) literature knowledge including rules on diabetes management [46].…”

Section: New-generation Projects and Studies In Diabetesmentioning

confidence: 99%

State of Art of Telemonitoring in Patients with Diabetes Mellitus, with a Focus on Elderly Patients

Andrès¹,

Meyer²,

Zulfiqar³

et al. 2019

Geriatric Medicine and Gerontology

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Since the beginning of the 1990s, several telemedicine projects and studies focused on type 1 and type 2 diabetes have been developed, including very few elderly diabetic patients. Several of these projects specifically concerned elderly subjects (n = 4). Mainly, these projects and studies show that telemonitoring diabetes results in improved blood glucose control-a significant reduction in HbA1c, improved patient ownership of the disease, greater patient adherence to therapeutic and hygiene-dietary measures, positive impact on comorbidities (hypertension, weight, dyslipidemia), improved quality of life for patients, and at least good patient receptivity and accountability. To date, the magnitude of its effects remains debatable, especially with the variation in patients' characteristics (e.g., background, ability for self-management, medical condition), sample selection, and approach for treatment of control groups. Over the last 5 years, numerous telemedicine projects based on connected objects and new information and communication technologies (ICT) (elements defining telemedicine 2.0) have emerged or are still under development.

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“…Therefore, diagnostic systems (Chen et al, 2018) have become more relevant and researchers such as Xia et al (2018) attempt to take on the challenge through the mining of information from sources such as DO, Symptom Ontology (SYMP) and MEDLINE/PubMed citation records. We can also observe in the literature a large volume of studies that use the mining of texts from different unstructured or semi-structured medical information sources (Frunza, Inkpen & Tran, 2011;Mazumder et al, 2016;Singhal, Simmons & Lu, 2016;Xu et al, 2016;Tsumoto et al, 2017;Sudeshna, Bhanumathi & Hamlin, 2017;Aich et al, 2017;Gupta et al, 2018;Rao & Rao, 2018;Zhao et al, 2018); (Bou Rjeily et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

DISNET: a framework for extracting phenotypic disease information from public sources

García

Rodríguez‐González

Santamaría

et al. 2020

PeerJ

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Background Within the global endeavour of improving population health, one major challenge is the identification and integration of medical knowledge spread through several information sources. The creation of a comprehensive dataset of diseases and their clinical manifestations based on information from public sources is an interesting approach that allows one not only to complement and merge medical knowledge but also to increase it and thereby to interconnect existing data and analyse and relate diseases to each other. In this paper, we present DISNET (http://disnet.ctb.upm.es/), a web-based system designed to periodically extract the knowledge from signs and symptoms retrieved from medical databases, and to enable the creation of customisable disease networks. Methods We here present the main features of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia and PubMed websites; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques. Results We further present the validation of our system on Wikipedia and PubMed texts, obtaining the relevant accuracy. The final output includes the creation of a comprehensive symptoms-disease dataset, shared (free access) through the system’s API. We finally describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses. Discussion DISNET allows retrieving knowledge about the signs, symptoms and diagnostic tests associated with a disease. It is not limited to a specific category (all the categories that the selected sources of information offer us) and clinical diagnosis terms. It further allows to track the evolution of those terms through time, being thus an opportunity to analyse and observe the progress of human knowledge on diseases. We further discussed the validation of the system, suggesting that it is good enough to be used to extract diseases and diagnostically-relevant terms. At the same time, the evaluation also revealed that improvements could be introduced to enhance the system’s reliability.

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Medical Data Mining for Heart Diseases and the Future of Sequential Mining in Medical Field

Cited by 47 publications

References 65 publications

Implementation of Machine Learning Model to Predict Heart Failure Disease

Implementation of Machine Learning Model to Predict Heart Failure Disease

State of Art of Telemonitoring in Patients with Diabetes Mellitus, with a Focus on Elderly Patients

DISNET: a framework for extracting phenotypic disease information from public sources

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