Data-driven diagnosis of spinal abnormalities using feature selection and machine learning algorithms

Raihan-Al-Masud, Md.; Mondal, M. Rubaiyat Hossain

doi:10.1371/journal.pone.0228422

Cited by 53 publications

(24 citation statements)

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“…Machine learning classifiers are successfully applied to classify normal (negative) cases and positive (having disease) cases for the case of many diseases [37][38][39][40][41][42][43][44][45][46][47][48][49][50]. Several classifiers such as RF [44], LR [43], SVM, XGBoost (XGB) are implemented in this work.…”

Section: Classification Algorithmsmentioning

confidence: 99%

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Forecasting the Spread of COVID-19 and ICU Requirements

Podder

Khamparia

Mondal

et al. 2021

Preprint

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Since December 2019, the world is fighting against coronavirus disease (COVID-19). This disease is caused by a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This work focuses on the applications of machine learning algorithms in the context of COVID-19. Firstly, regression analysis is performed to model the number of confirmed cases and death cases. Our experiments show that autoregressive integrated moving average (ARIMA) can reliably model the increase in the number of confirmed cases and can predict future cases. Secondly, a number of classifiers are used to predict whether a COVID-19 patient needs to be admitted to an intensive care unit (ICU) or semi-ICU. For this, classification algorithms are applied to a dataset having 5644 samples. Using this dataset, the most significant attributes are selected using features selection by ExtraTrees classifier, and Proteina C reativa (mg/dL) is found to be the highest-ranked feature. In our experiments, random forest, logistic regression, support vector machine, XGBoost, stacking and voting classifiers are applied to the top 10 selected attributes of the dataset. Results show that random forest and hard voting classifiers achieve the highest classification accuracy values near 98%, and the highest recall value of 98% in predicting the need for admission into ICU/semi ICU units.

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Section: Classification Algorithmsmentioning

confidence: 99%

“…These classifiers are often used in cancer disease prediction such as breast cancer, lung cancer, etc. [39,42], prediction of spinal abnormalities [37] and hepatitis disease prediction [38]. Therefore, these algorithms are applied to the dataset.…”

Section: Classification Algorithmsmentioning

confidence: 99%

Forecasting the Spread of COVID-19 and ICU Requirements

Podder

Khamparia

Mondal

et al. 2021

Preprint

Self Cite

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“…In this paper, experiments are performed to classify patients who need admission to ICU or semi-ICU and patients who do not need such admission. The classification is done using the samples available in the dataset publicly available Kaggle repository [37]. 5644 samples are contained in this dataset with 111 attributes provided by Hospital Israelita Albert Einstein, Brazil.…”

Section: Regression Analysismentioning

confidence: 99%

Section: Classification Algorithmsmentioning

confidence: 99%

Forecasting the Spread of COVID-19 and ICU Requirements

et al. 2021

Self Cite

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<span lang="EN-US">Since December 2019, the world is fighting against coronavirus disease (COVID-19). This disease is caused by a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This work focuses on the applications of machine learning algorithms in the context of COVID-19. Firstly, regression analysis is performed to model the number of confirmed cases and death cases. Our experiments show that autoregressive integrated moving average (ARIMA) can reliably model the increase in the number of confirmed cases and can predict future cases. Secondly, a number of classifiers are used to predict whether a COVID-19 patient needs to be admitted to an intensive care unit (ICU) or semi-ICU. For this, classification algorithms are applied to a dataset having 5644 samples. Using this dataset, the most significant attributes are selected using features selection by ExtraTrees classifier, and Proteina C reativa (mg/dL) is found to be the highest-ranked feature. In our experiments, random forest, logistic regression, support vector machine, XGBoost, stacking and voting classifiers are applied to the top 10 selected attributes of the dataset. Results show that random forest and hard voting classifiers achieve the highest classification accuracy values near 98%, and the highest recall value of 98% in predicting the need for admission into ICU/semi ICU units.</span>

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“…In [13], numerous classifiers such as Support Vector Machine (SVM), logistic regression (LR), bagging ensemble methods are applied for the diagnosis of spinal abnormality. Before implementing these classifier models, data pre-processing step are carried out.…”

Section: Related Work-mentioning

confidence: 99%

Detecting Lower Back Pain Using Stacked Ensemble Approach

Bandyopadhyay¹,

Dutta²

2020

Preprint

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Lower Back Pain (LBP) is a disease that needs immediate attention. Person with back pain shall go immediately to doctor for treatment. Injury, excessive works and some medical conditions are result of back pain. Back pain is common to any age of human for different reasons. Due to factors such as previous occupation and degenerative disk disease the chance of developing lower back pain increases for older people. It hampers the working condition of people common reason for seeking medical treatment. The result is absence from work and is unable to normal due to pain. It creates uncomfortable and debilitating situations. Hence, detecting this disease at an early stage will assist the medical field experts to suggest counter measures to the patients. Detection of lower back pain is implemented in this paper by applying ensemble machine learning technique. This paper proposes Stacking ensemble classifier as an automated tool that will predict lower back pain tendency of a patient. Experimental result implies that the proposed method reaches an accuracy of 76.34%, f1-score of 0.76 and MSE of 0.34.

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Data-driven diagnosis of spinal abnormalities using feature selection and machine learning algorithms

Cited by 53 publications

References 24 publications

Forecasting the Spread of COVID-19 and ICU Requirements

Forecasting the Spread of COVID-19 and ICU Requirements

Forecasting the Spread of COVID-19 and ICU Requirements

Detecting Lower Back Pain Using Stacked Ensemble Approach

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