Evidence map›Paper›PMID 35859929›Full record

ArticleJournal of healthcare engineering2022

A Context-Aware MRIPPER Algorithm for Heart Disease Prediction.

Saad Almutairi, S Manimurugan, Naveen Chilamkurti, Majed Mohammed Aborokbah, C Narmatha, Subramaniam Ganesan, Riyadh A Alzaheb, Hani Almoamari

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In one paragraph

Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Saad AlmutairiIndustrial Innovation and Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia.ORCID 0000-0002-1320-4665
S ManimuruganIndustrial Innovation and Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia.ORCID 0000-0003-1837-6797
Naveen ChilamkurtiDepartment of Computer Science and IT, La Trobe University, Melbourne, Australia.ORCID 0000-0002-5396-8897
Majed Mohammed AborokbahIndustrial Innovation and Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia.
C NarmathaIndustrial Innovation and Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia.ORCID 0000-0003-4411-4045
Subramaniam GanesanDepartment of Electrical and Computer Engineering, Oakland University, Rochester, NY, USA.ORCID 0000-0003-0233-9940
Riyadh A AlzahebFaculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Hani AlmoamariFaculty of Computer and Information Systems, Islamic University of Madinah, Medina, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

These days, mobile computing devices are ubiquitous and are widely used in almost every facet of daily life. In addition, computing and the modern technologies are not really coexisting anymore. With a wide range of conditions and areas of concern, the medical domain was also concerned. New types of technologies, such as context-aware systems and applications, are constantly being infused into the medicine field. An IoT-enabled healthcare system based on context awareness is developed in this work. In order to collect and store the patient data, smart medical devices are employed. Context-aware data from the database includes the patient's medical records and personal information. The MRIPPER (Modified Repeated Incremental Pruning to Produce Error) technique is used to analyze and classify the data. A rule-based machine learning method is used in this algorithm. The rules for analyzing datasets in order to make predictions about heart disease are framed using this algorithm. MATLAB is used to simulate the proposed model's performance analysis. Other models like random forest, J48, CART, JRip, and OneR algorithms are also compared to validate the proposed model's performance. The proposed model obtains 98.89 percent accuracy, 96.76 percent precision, 99.05 percent sensitivity, 94.35 percent specificity, and 97.60 percent f-score. Predictions for subjects in the normal and abnormal classes were both accurate with 97.38 for normal and 97.93 for abnormal subjects.

Indexed as

AlgorithmsHeart DiseasesDatabases, FactualHumansMachine Learning

Identifiers

PMID35859929
PMCPMC9293545

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.