Parul Setiya

Current Organizations / Institutes

The organizations/institutes from the scholar's latest article

Govind Ballabh Pant University of Agriculture and Technology

Research Subjects

Research Interests

Parul Setiya has a total of 29 co-authors. Here are the top co-authors by number of shared publications:

Ahmad Alsaber
Department of Management, American University of Kuwait, Salmiya, Kuwait
3 shared publications
Gary Tse
School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China
2 shared publications
Mohammed Al Jarallah
Department of Cardiology, Sabah Al Ahmed Cardiac Centre, Al Amiri Hospital, Kuwait City, Kuwait
2 shared publications
Peter A. Brady
ZOLL Corporation, Vitebsk City Clinical Hospital of Emergency Medical Care, Vitebsk, Belarus
2 shared publications
Raja Dashti
Department of Cardiology, Sabah Al Ahmed Cardiac Centre, Al Amiri Hospital, Kuwait City, Kuwait
2 shared publications
Rajesh Rajan
Department of Cardiology, Sabah Al Ahmad Cardiac Center, Al Amiri Hospital, Sharq, Kuwait
2 shared publications

Latest Publications

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Cited by 10
- 31 December 2023
- Published by India Meteorological Department in MAUSAM
Comparative analysis of SMLR, ANN, Elastic net and LASSO based models for rice crop yield prediction in Uttarakhand
by Parul Setiya, Ajeet Singh Nain, Anurag Satpathi
The study was aimed to develop the yield forecast model for rice crop yield. Four different techniques i.e. Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN), Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ELNET)were used to build the prediction models. Dataset of meteorological data and crop yield data of 15 years have been used to develop the forecast models. The developed models were also validated on the dataset of three years. The assessment of the developed models wasdone by using root mean square error (RMSE),normalized root mean square error (nRMSE),Mean Absolute Error (MAE) and on the basis of coefficient of determination (R2). The experimental analysis suggested that the performance for Artificial Neural Network (R2=0.99, RMSE=0.07, nRMSE=2.20, MAE=0.06) is better as compared to SMLR(R2=0.97, RMSE=0.08, nRMSE=2.34, MAE=0.05), LASSO (R2=0.62, RMSE=0.26, nRMSE=7.81, MAE=0.24) and ELNET (R2=0.54, RMSE=0.38, nRMSE=11.41, MAE=0.37) for the predictionof rice crop yield for Udham Singh Nagar (USN) district of Uttarakhand. Therefore, for the prediction of rice yield, ANN technique can be well utilised for Udham Singh Nagar district of Uttarakhand.
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- 31 December 2023
- Published by India Meteorological Department in MAUSAM
Open Access
Comparative analysis of SMLR, ANN, Elastic net and LASSO based models for rice crop yield prediction in Uttarakhand
by Parul Setiya, Ajeet Singh Nain, Anurag Satpathi
The study was aimed to develop the yield forecast model for rice crop yield. Four different techniques i.e. Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN), Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ELNET)were used to build the prediction models. Dataset of meteorological data and crop yield data of 15 years have been used to develop the forecast models. The developed models were also validated on the dataset of three years. The assessment of the developed models wasdone by using root mean square error (RMSE),normalized root mean square error (nRMSE),Mean Absolute Error (MAE) and on the basis of coefficient of determination (R2). The experimental analysis suggested that the performance for Artificial Neural Network (R2=0.99, RMSE=0.07, nRMSE=2.20, MAE=0.06) is better as compared to SMLR(R2=0.97, RMSE=0.08, nRMSE=2.34, MAE=0.05), LASSO (R2=0.62, RMSE=0.26, nRMSE=7.81, MAE=0.24) and ELNET (R2=0.54, RMSE=0.38, nRMSE=11.41, MAE=0.37) for the predictionof rice crop yield for Udham Singh Nagar (USN) district of Uttarakhand. Therefore, for the prediction of rice yield, ANN technique can be well utilised for Udham Singh Nagar district of Uttarakhand.
Cited by 10
Open Access
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Cited by 2
- 5 December 2023
- Published by Wolters Kluwer Health in Annals of Medicine & Surgery
Transcatheter aortic valve replacement in patients with chronic kidney disease: a multi-centre retrospective study
by Mohammed Al Jarallah, Salman Abdulsalam, Rajesh Rajan, Raja Dashti, Kobalava D. Zhanna, Parul Setiya, Ahmad Al-Saber, Mohammad Alajmi, Peter A. Brady, Georgiana Luisa Baca, et al.
Background: Chronic kidney disease (CKD) is a common comorbid condition in patients undergoing transcatheter aortic valve replacement (TAVR). Reported outcome studies on the association of baseline CKD and mortality is currently limited. Objectives: To determine the prevalence of chronic kidney disease in patients undergoing TAVR and analyse their overall procedural outcomes. Methods: This retrospective observational study was conducted at 43 publicly funded hospitals in Hong Kong. Severe aortic stenosis patients undergoing TAVR between the years 2010 and 2019 were enroled in the study. Two groups were identified according to the presence of baseline chronic kidney disease. Results: A total of 499 patients (228, 58.6% men) were enroled in the study. Baseline hypertension was more prevalent in patients with CKD (82.8%; P=0.003). As for primary end-points, mortality rates of CKD patients were significantly higher compared to non-CKD patients (10% vs. 4.1%; P=0.04%). Gout and hypertension were found to be significantly associated with CRF. Patients with gout were nearly six times more likely to have CRF than those without gout (odds ratio = 5.96, 95% CI = 3.12–11.29, PP=0.004). Conclusion: In patients with severe aortic stenosis undergoing TAVR, baseline CKD significantly contributes to mortality outcomes at long-term follow up.
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- 5 December 2023
- Published by Wolters Kluwer Health in Annals of Medicine & Surgery
Open Access
Transcatheter aortic valve replacement in patients with chronic kidney disease: a multi-centre retrospective study
by Mohammed Al Jarallah, Salman Abdulsalam, Rajesh Rajan, Raja Dashti, Kobalava D. Zhanna, Parul Setiya, Ahmad Al-Saber, Mohammad Alajmi, Peter A. Brady, Georgiana Luisa Baca, et al.
Background: Chronic kidney disease (CKD) is a common comorbid condition in patients undergoing transcatheter aortic valve replacement (TAVR). Reported outcome studies on the association of baseline CKD and mortality is currently limited. Objectives: To determine the prevalence of chronic kidney disease in patients undergoing TAVR and analyse their overall procedural outcomes. Methods: This retrospective observational study was conducted at 43 publicly funded hospitals in Hong Kong. Severe aortic stenosis patients undergoing TAVR between the years 2010 and 2019 were enroled in the study. Two groups were identified according to the presence of baseline chronic kidney disease. Results: A total of 499 patients (228, 58.6% men) were enroled in the study. Baseline hypertension was more prevalent in patients with CKD (82.8%; P=0.003). As for primary end-points, mortality rates of CKD patients were significantly higher compared to non-CKD patients (10% vs. 4.1%; P=0.04%). Gout and hypertension were found to be significantly associated with CRF. Patients with gout were nearly six times more likely to have CRF than those without gout (odds ratio = 5.96, 95% CI = 3.12–11.29, PP=0.004). Conclusion: In patients with severe aortic stenosis undergoing TAVR, baseline CKD significantly contributes to mortality outcomes at long-term follow up.
Cited by 2
Open Access
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Cited by 89
- 3 February 2023
- Published by MDPI AG in Sustainability
Comparative Analysis of Statistical and Machine Learning Techniques for Rice Yield Forecasting for Chhattisgarh, India
by Anurag Satpathi, Parul Setiya, Bappa Das, Ajeet Singh Nain, Prakash Kumar Jha, Surendra Singh, Shikha Singh
Crop yield forecasting before harvesting is critical for the creation, implementation, and optimization of policies related to food safety as well as for agro-product storage and marketing. Crop growth and development are influenced by the weather. Therefore, models using weather variables can provide reliable predictions of crop yields. It can be tough to select the best crop production forecasting model. Therefore, in this study, five alternative models, viz., stepwise multiple linear regression (SMLR), an artificial neural network (ANN), the least absolute shrinkage and selection operator (LASSO), an elastic net (ELNET), and ridge regression, were compared in order to discover the best model for rice yield prediction. The outputs from individual models were used to build ensemble models using the generalized linear model (GLM), random forest (RF), cubist and ELNET methods. For the previous 21 years, historical rice yield statistics and meteorological data were collected for three districts under three separate agro-climatic zones of Chhattisgarh, viz., Raipur in the Chhattisgarh plains, Surguja in the northern hills, and Bastar in the southern plateau. The models were calibrated using 80% of these datasets, and the remaining 20% was used for the validation of models. The present study concluded that for rice crop yield forecasting, the performance of the ANN was good for the Raipur ( $R_{c a l}^{2}$ = 1, $R_{v a l}^{2}$ = 1 and $R M S E_{c a l}$ = 0.002, $R M S E_{v a l}$ = 0.003) and Surguja ( $R_{c a l}^{2}$ = 1, $R_{v a l}^{2}$ = 0.99 and $R M S E_{c a l}$ = 0.004, $R M S E_{v a l}$ = 0.214) districts as compared to the other models, whereas for Bastar, ELNET ( $R_{c a l}^{2}$ = 90, $R_{v a l}^{2}$ = 0.48) and LASSO ( $R_{c a l}^{2}$ = 93, $R_{v a l}^{2}$ = 0.568) performed better. The performance of the ensemble model was better compared to the individual models. For Raipur and Surguja, the performance of all the ensemble methods was comparable, whereas for Bastar, random forest (RF) performed better, with R² = 0.85 and 0.81 for calibration and validation, respectively, as compared to the GLM, cubist, and ELNET approach.
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- 3 February 2023
- Published by MDPI AG in Sustainability
Open Access
Comparative Analysis of Statistical and Machine Learning Techniques for Rice Yield Forecasting for Chhattisgarh, India
by Anurag Satpathi, Parul Setiya, Bappa Das, Ajeet Singh Nain, Prakash Kumar Jha, Surendra Singh, Shikha Singh
Crop yield forecasting before harvesting is critical for the creation, implementation, and optimization of policies related to food safety as well as for agro-product storage and marketing. Crop growth and development are influenced by the weather. Therefore, models using weather variables can provide reliable predictions of crop yields. It can be tough to select the best crop production forecasting model. Therefore, in this study, five alternative models, viz., stepwise multiple linear regression (SMLR), an artificial neural network (ANN), the least absolute shrinkage and selection operator (LASSO), an elastic net (ELNET), and ridge regression, were compared in order to discover the best model for rice yield prediction. The outputs from individual models were used to build ensemble models using the generalized linear model (GLM), random forest (RF), cubist and ELNET methods. For the previous 21 years, historical rice yield statistics and meteorological data were collected for three districts under three separate agro-climatic zones of Chhattisgarh, viz., Raipur in the Chhattisgarh plains, Surguja in the northern hills, and Bastar in the southern plateau. The models were calibrated using 80% of these datasets, and the remaining 20% was used for the validation of models. The present study concluded that for rice crop yield forecasting, the performance of the ANN was good for the Raipur ( $R_{c a l}^{2}$ = 1, $R_{v a l}^{2}$ = 1 and $R M S E_{c a l}$ = 0.002, $R M S E_{v a l}$ = 0.003) and Surguja ( $R_{c a l}^{2}$ = 1, $R_{v a l}^{2}$ = 0.99 and $R M S E_{c a l}$ = 0.004, $R M S E_{v a l}$ = 0.214) districts as compared to the other models, whereas for Bastar, ELNET ( $R_{c a l}^{2}$ = 90, $R_{v a l}^{2}$ = 0.48) and LASSO ( $R_{c a l}^{2}$ = 93, $R_{v a l}^{2}$ = 0.568) performed better. The performance of the ensemble model was better compared to the individual models. For Raipur and Surguja, the performance of all the ensemble methods was comparable, whereas for Bastar, random forest (RF) performed better, with R² = 0.85 and 0.81 for calibration and validation, respectively, as compared to the GLM, cubist, and ELNET approach.
Cited by 89
Open Access
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Cited by 6
- 7 May 2022
- Published by Wolters Kluwer Health in Annals of Medicine & Surgery
Clinical outcomes of transcatheter aortic valve replacement stratified by left ventricular ejection fraction: A single centre pilot study
by Joud Al Balool, Mohammed Al Jarallah, Rajesh Rajan, Raja Dashti, Nader Alasousi, Vladimir Kotevski, Ahmed Said Taha Mousa, Retaj Al Haroun, Gary Tse, Kobalava D. Zhanna, et al.
Introduction: To define baseline echocardiographic, electrocardiographic (ECG) and computed tomographic (CT) findings of patients with heart failure undergoing transcatheter aortic valve replacement (TAVR) and analyze their overall procedural outcomes. Methods: Between 2018 and 2021, patients with severe aortic stenosis (AS) who performed transcatheter aortic valve replacement (TAVR) in Sabah Al Ahmad Cardiac Centre, Al Amiri Hospital were identified. A retrospective review of patients' parameters including pre-, intra-, and post-procedural data was conducted. Patients were grouped in 2 subgroups according to their EF: EF 40%. The data included patients' baseline characteristics, electrocardiographic and echocardiographic details along with pre-procedural CT assessment of aortic valve dimensions. Primary outcomes including post-operative disturbances, pacemaker implantation and in-hospital mortality following TAVR were additionally analyzed. Results: A total of 61 patients with severe AS underwent TAVR. The mean age was 73.5 +/- 9, and 21 (34%) of the patients were males. The mean ejection fraction (EF) was 55.5 +/- 9.7%. Of 61 patients, 12 (20%) were identified as heart failure with reduced EF (<40%). These patients were younger, more often males, and were more likely to have coronary artery disease (75% versus 53.1%). Left ventricular hypertrophy and diastolic dysfunction was documented in 75% and 58.3% of patients with heart failure with reduced ejection fraction (HFrEF) respectively. Post TAVR conduction disturbances, with the commonest being LBBB was observed in 41.7%. Permanent pacemaker was implanted in 3 of patients with HFrEF (25%). There were no significant differences between the two groups with regards to in hospital mortality (p = 0.618). Conclusion: Severe AS with EF 40%.
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- 7 May 2022
- Published by Wolters Kluwer Health in Annals of Medicine & Surgery
Open Access
Clinical outcomes of transcatheter aortic valve replacement stratified by left ventricular ejection fraction: A single centre pilot study
by Joud Al Balool, Mohammed Al Jarallah, Rajesh Rajan, Raja Dashti, Nader Alasousi, Vladimir Kotevski, Ahmed Said Taha Mousa, Retaj Al Haroun, Gary Tse, Kobalava D. Zhanna, et al.
Introduction: To define baseline echocardiographic, electrocardiographic (ECG) and computed tomographic (CT) findings of patients with heart failure undergoing transcatheter aortic valve replacement (TAVR) and analyze their overall procedural outcomes. Methods: Between 2018 and 2021, patients with severe aortic stenosis (AS) who performed transcatheter aortic valve replacement (TAVR) in Sabah Al Ahmad Cardiac Centre, Al Amiri Hospital were identified. A retrospective review of patients' parameters including pre-, intra-, and post-procedural data was conducted. Patients were grouped in 2 subgroups according to their EF: EF 40%. The data included patients' baseline characteristics, electrocardiographic and echocardiographic details along with pre-procedural CT assessment of aortic valve dimensions. Primary outcomes including post-operative disturbances, pacemaker implantation and in-hospital mortality following TAVR were additionally analyzed. Results: A total of 61 patients with severe AS underwent TAVR. The mean age was 73.5 +/- 9, and 21 (34%) of the patients were males. The mean ejection fraction (EF) was 55.5 +/- 9.7%. Of 61 patients, 12 (20%) were identified as heart failure with reduced EF (<40%). These patients were younger, more often males, and were more likely to have coronary artery disease (75% versus 53.1%). Left ventricular hypertrophy and diastolic dysfunction was documented in 75% and 58.3% of patients with heart failure with reduced ejection fraction (HFrEF) respectively. Post TAVR conduction disturbances, with the commonest being LBBB was observed in 41.7%. Permanent pacemaker was implanted in 3 of patients with HFrEF (25%). There were no significant differences between the two groups with regards to in hospital mortality (p = 0.618). Conclusion: Severe AS with EF 40%.
Cited by 6
Open Access
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Cited by 59
- 8 January 2020
- Published by MDPI AG in International Journal of Environmental Research and Public Health
Influence of Ambient Air Pollution on Rheumatoid Arthritis Disease Activity Score Index
by Ahmad Alsaber, Jiazhu Pan, Adeeba Al-Herz, Dhary S. Alkandary, Adeeba Al-Hurban, Parul Setiya, on behalf of the KRRD Group
Rheumatoid arthritis (RA) is a chronic autoimmune of an unknown etiology. Air pollution has been proposed as one of the possible risk factors associated with disease activity, although has not been extensively studied. In this study, we measured the relationship between exposure to air pollutants and RA activity. Data on RA patients were extracted from the Kuwait Registry for Rheumatic Diseases (KRRD). Disease activity was measured using disease activity score with 28 examined joints (DAS-28) and the Clinical Disease Activity Index (CDAI) during their hospital visits from 2013 to 2017. Air pollution was assessed using air pollution components (PM $_{10}$ , NO $_{2}$ , SO $_{2}$ , O $_{3}$ , and CO). Air pollution data were obtained from Kuwait Environmental Public Authority (K-EPA) from six different air quality-monitoring stations during the same period. Multiple imputations by the chained equations (MICE) algorithm were applied to estimate missing air pollution data. Patients data were linked with air pollution data according to date and patient governorate address. Descriptive statistics, correlation analysis, and linear regression techniques were employed using STATA software. In total, 1651 RA patients with 9875 follow-up visits were studied. We detected an increased risk of RA using DAS-28 in participants exposed to SO $_{2}$ and NO $_{2}$ with $β = 0.003$ (95% CI: 0.0004–0.005, $p < 0.01$ ) and $β = 0.003$ (95% CI: 0.002–0.005, $p < 0.01$ ), respectively, but not to PM $_{10}$ , O $_{3}$ , and CO concentrations. Conclusively, we observed a strong association between air pollution with RA disease activity. This study suggests air pollution as a risk factor for RA and recommends further measures to be taken by the authorities to control this health problem.
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- 8 January 2020
- Published by MDPI AG in International Journal of Environmental Research and Public Health
Open Access
Influence of Ambient Air Pollution on Rheumatoid Arthritis Disease Activity Score Index
by Ahmad Alsaber, Jiazhu Pan, Adeeba Al-Herz, Dhary S. Alkandary, Adeeba Al-Hurban, Parul Setiya, on behalf of the KRRD Group
Rheumatoid arthritis (RA) is a chronic autoimmune of an unknown etiology. Air pollution has been proposed as one of the possible risk factors associated with disease activity, although has not been extensively studied. In this study, we measured the relationship between exposure to air pollutants and RA activity. Data on RA patients were extracted from the Kuwait Registry for Rheumatic Diseases (KRRD). Disease activity was measured using disease activity score with 28 examined joints (DAS-28) and the Clinical Disease Activity Index (CDAI) during their hospital visits from 2013 to 2017. Air pollution was assessed using air pollution components (PM $_{10}$ , NO $_{2}$ , SO $_{2}$ , O $_{3}$ , and CO). Air pollution data were obtained from Kuwait Environmental Public Authority (K-EPA) from six different air quality-monitoring stations during the same period. Multiple imputations by the chained equations (MICE) algorithm were applied to estimate missing air pollution data. Patients data were linked with air pollution data according to date and patient governorate address. Descriptive statistics, correlation analysis, and linear regression techniques were employed using STATA software. In total, 1651 RA patients with 9875 follow-up visits were studied. We detected an increased risk of RA using DAS-28 in participants exposed to SO $_{2}$ and NO $_{2}$ with $β = 0.003$ (95% CI: 0.0004–0.005, $p < 0.01$ ) and $β = 0.003$ (95% CI: 0.002–0.005, $p < 0.01$ ), respectively, but not to PM $_{10}$ , O $_{3}$ , and CO concentrations. Conclusively, we observed a strong association between air pollution with RA disease activity. This study suggests air pollution as a risk factor for RA and recommends further measures to be taken by the authorities to control this health problem.
Cited by 59