Novel Approach to Evaluate Classification Algorithms and Feature Selection Filter Algorithms Using Medical Data

Masood, Fawad; Masood, Junaid; Zahir, H.; Driss, Kaouthar; Mehmood, Nasir; Farooq, Hassan

doi:10.47852/bonviewjcce2202238

Cited by 42 publications

(21 citation statements)

References 14 publications

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“…The reason could be the fact that the continuous care model has the potential to mitigate negative symptoms such as fatigue and insomnia in patients, which, in turn, can enhance their emotional state and improve their overall life quality. [20][21][22] Specialized nurses can reduce patients negative emotions and improve their life quality by guiding them to complete simple and moderate aerobic exercise, as well as a healthy lifestyle, comfortable sleep environment, and correct concepts. This was consistent with the nursing research results of scholars on lung cancer patients.…”

Section: Discussionmentioning

confidence: 99%

Life quality improvement of patients with non-small cell lung cancer undergoing targeted therapy: A case study of continuous care

Min,

Zhu,

et al. 2023

Medicine

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To investigate the improvement effect of targeted therapy on non-small cell carcinoma patients life quality after the continuous nursing intervention. 104 non-small cell lung cancer patients in our hospital from July 2017 to November 2019 were allocated evenly and randomly into the control group (C) and the study group (S). By using clinical baseline data, quality of life questionnaire core 30 for cancer patients, evaluation of patient compliance behavior, the MOS item short-form health survey (SF-36), self rating depression scale (SDS), self rating anxiety scale (SAS), Overall Survival (OS) progression-free survival and adverse reaction symptoms were evaluated for the life quality of patients. There was comparability between the 2 sets of basic data. There was no significant difference in quality of life questionnaire core 30, SF-36, SAS, or SDS scores before treatment. After 3 months, there was a significant difference in the scores of various scales before treatment. At the same time, there was significant statistical significance before and after treatment in Group S. Their compliance rates were 84.62% and 98.08%. Adverse reactions incidence in Group S was lower. Taking a 2-year follow-up period as an example, significant statistical differences existed in OS and progression-free survival rates between adenocarcinoma and squamous carcinoma. SDS and SAS had high consistency in scoring with QLQ-30 and SF-36 scales. Targeted treatment for non-small cell carcinoma patients significantly improves their life quality and reduces the incidence of adverse reactions after continuous nursing intervention.

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Section: Discussionmentioning

confidence: 99%

Life quality improvement of patients with non-small cell lung cancer undergoing targeted therapy: A case study of continuous care

Min,

Zhu,

et al. 2023

Medicine

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“…These techniques are mainly used to predict and classify variables of interest (supervised learning) through feature selection . 48-50 Both processes are subfields of data mining. 51,52…”

Section: Network Analysismentioning

confidence: 99%

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis

García-Jaramillo,

Luque,

León-Vargas

2023

J Diabetes Sci Technol

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Background: The use of machine learning and deep learning techniques in the research on diabetes has garnered attention in recent times. Nonetheless, few studies offer a thorough picture of the knowledge generation landscape in this field. To address this, a bibliometric analysis of scientific articles published from 2000 to 2022 was conducted to discover global research trends and networks and to emphasize the most prominent countries, institutions, journals, articles, and key topics in this domain. Methods: The Scopus database was used to identify and retrieve high-quality scientific documents. The results were classified into categories of detection (covering diagnosis, screening, identification, segmentation, among others), prediction (prognosis, forecasting, estimation), and management (treatment, control, monitoring, education, telemedicine integration). Biblioshiny and RStudio were used to analyze the data. Results: A total of 1773 articles were collected and analyzed. The number of publications and citations increased substantially since 2012, with a notable increase in the last 3 years. Of the 3 categories considered, detection was the most dominant, followed by prediction and management. Around 53.2% of the total journals started disseminating articles on this subject in 2020. China, India, and the United States were the most productive countries. Although no evidence of outstanding leadership by specific authors was found, the University of California emerged as the most influential institution for the development of scientific production. Conclusion: This is an evolving field that has experienced a rapid increase in productivity, especially over the last years with exponential growth. This trend is expected to continue in the coming years.

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“…The major reason for this phenomenon is clueless presence of glaucoma at early stages so that treatment might be started. The combination of image processing and machine learning has shown good results in various fields of medical science and healthcare systems 4–7 . Medical imaging techniques, when combined with machine learning and image processing, can significantly enhance the early detection and diagnosis of retinal diseases.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based Glaucoma Network Classification (GNC) using retinal images

Kiyani,

Shehryar,

Khalid

et al. 2023

Int J Imaging Syst Tech

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The proposed deep learning framework for glaucoma classification addresses critical challenges of limited data and computational costs. Employing data augmentation and normalization techniques, the three‐stage model, utilizing InceptionV3 and ResNet50, achieves high training (99.3% ‐ 99.8%) and testing accuracy (91.6% ‐ 92.12%) on a dataset comprising 16,328 images from fused public datasets. This outperforms existing automated models. The approach leverages transfer learning and convolutional neural networks, showcasing its potential for accurate and timely glaucoma diagnosis. However, ongoing validation on diverse datasets and ethical considerations regarding fairness and transparency in medical applications remain essential. The model's reliability suggests its promising role in aiding early glaucoma detection, potentially averting irreversible vision impairment.

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Novel Approach to Evaluate Classification Algorithms and Feature Selection Filter Algorithms Using Medical Data

Cited by 42 publications

References 14 publications

Life quality improvement of patients with non-small cell lung cancer undergoing targeted therapy: A case study of continuous care

Life quality improvement of patients with non-small cell lung cancer undergoing targeted therapy: A case study of continuous care

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis

Deep learning based Glaucoma Network Classification (GNC) using retinal images

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