Applications, Opportunities, and Current Challenges in the Healthcare Industry

Veena, A.; Gowrishankar, S.

doi:10.1201/9781003217435-2

Cited by 2 publications

(1 citation statement)

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“…There are times when the logistical aspects of data collection present some challenges preventing data collection (Sadoon, Venkatadri & Ghasemi, 2023;Veena & Gowrishankar, 2023): i.…”

Section: Practical Challengesmentioning

confidence: 99%

Challenges in research data collection and mitigation interventions

Menziwa,

Sesale,

Seeletse

2024

IJRBS

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This paper investigated the challenges that researchers in a health sciences university can experience, and ways to counterbalance the negative effects of these challenges. Focus was on the extent to which gatekeepers on higher education institutions (HEIs) can restrict research, and the way natural sciences researchers often experience gatekeeper biasness on denying them access as compared to the way health sciences researchers are treated. The method compared experiences of researchers for Master of Science (MSc) degrees in selected science subjects, and the projects undertaken by health sciences students. All the studies were based on students on campus as research subjects. The MSc ones were for students who were already academics teaching on campus. All the proposals received clearance certificates from the same ethics committee. Upon requiring the HEI registrar to grant permission to use the student as study participants, the health sciences were granted permission and the names of the students. For the science academics, they were denied permission to the student numbers, which were needed to request individual students to make on decisions whether they wanted to participate in the studies or not. Gatekeeping weaknesses were explored, and lawful interventions were used to collect research data. It was observed that in the science academic divisions of HEIs that are dominated by the health sciences, gatekeeper unfairness and power could offset creativities and innovations initiated by researchers. Recommendations have been made to limit this power.

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“…There are times when the logistical aspects of data collection present some challenges preventing data collection (Sadoon, Venkatadri & Ghasemi, 2023;Veena & Gowrishankar, 2023): i.…”

Section: Practical Challengesmentioning

confidence: 99%

Challenges in research data collection and mitigation interventions

Menziwa,

Sesale,

Seeletse

2024

IJRBS

View full text Add to dashboard Cite

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Augmenting Chronic Kidney Disease Diagnosis With Support Vector Machines for Improved Classifier Accuracy

Kumar,

Gnanaguru

et al. 2024

Advances in Medical Technologies and Clinical Practice

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Mitigating chronic kidney disease poses a substantial challenge to the healthcare community. This study assesses diverse classification algorithms, encompassing NaiveBayes, multi-layer perceptron, and support vector machine. The analysis involves scrutinizing the chronic kidney disease dataset from the UCI machine learning repository. Techniques like replacing missing values, unsupervised discretization, and normalization are employed for precision enhancement. The empirical results of the classification models are evaluated for accuracy and computational time. The conclusive observation indicates that the support vector machine performs notably better than all other classification methods, with a 76% classifier accuracy which is better than classifiers such as MLP and NB. The lack of application of those feature selection methods to the dataset is a drawback of this study.

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Applications, Opportunities, and Current Challenges in the Healthcare Industry

Cited by 2 publications

References 0 publications

Challenges in research data collection and mitigation interventions

Challenges in research data collection and mitigation interventions

Augmenting Chronic Kidney Disease Diagnosis With Support Vector Machines for Improved Classifier Accuracy

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