Heart Disease Prediction Using Stacking Model With Balancing Techniques and Dimensionality Reduction

Heart disease is a serious worldwide health issue with wide-reaching effects. Since heart disease is one of the leading causes of mortality worldwide, early detection is crucial. Emerging technologies like Machine Learning (ML) are currently being actively used by the biomedical, healthcare, and health prediction industries. PaRSEL, a new stacking model is proposed in this research, that combines four classifiers, Passive Aggressive Classifier (PAC), Ridge Classifier (RC), Stochastic Gradient Descent Classifier (SGDC), and eXtreme Gradient Boosting (XGBoost), at the base layer, and LogitBoost is deployed for the final predictions at the meta layer. The imbalanced and irrelevant features in the data increase the complexity of the classification models. The dimensionality reduction and data balancing approaches are considered very important for lowering costs and increasing the accuracy of the model. In PaRSEL, three dimensionality reduction techniques, Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and Factor Analysis (FA), are used to reduce the dimensionality and select the most relevant features for the diagnosis of heart disease. Furthermore, eight balancing techniques, Proximity Weighted Random Affine Shadowsampling (ProWRAS), Localized Randomized Affine Shadowsampling (LoRAS), Random Over Sampling (ROS), Adaptive Synthetic (ADASYN), Synthetic Minority Oversampling Technique (SMOTE), Borderline SMOTE (B-SMOTE), Majority Weighted Minority Oversampling Technique (MWMOTE) and Random Walk Oversampling (RWOS), are used to deal with the imbalanced nature of the dataset. The performance of PaRSEL is compared with the other standalone classifiers using different performance measures like accuracy, F1-score, precision, recall and AUC-ROC score. Our proposed model achieves 97% accuracy, 80% F1-score, precision is greater than 90%, 67% recall, and 98% AUC-ROC score. This shows that PaRSEL outperforms other standalone classifiers in terms of heart disease prediction. Additionally, we deploy SHapley Additive exPlanations (SHAP) on our proposed model. It helps to understand the internal working of the model. It illustrates how much influence a classifier has on the final prediction outcome

Superimposed Training based Estimation of Sparse MIMO Channels for Emerging Wireless Networks

Multiple-input multiple-output (MIMO) systems constitute an important part of todays wireless communication standards and these systems are expected to take a fundamental role in both the access and backhaul sides of the emerging wireless cellular networks. Recently, reported measurement campaigns have established that various outdoor radio propagation environments exhibit sparsely structured channel impulse response (CIR). We propose a novel superimposed training (SiT) based up-link channels’ estimation technique for multipath sparse MIMO communication channels using a matching pursuit (MP) algorithm; the proposed technique is herein named as superimposed matching pursuit (SI-MP). Subsequently, we evaluate the performance of the proposed technique in terms of mean-square error (MSE) and bit-error-rate (BER), and provide its comparison with that of the notable first order statistics based superimposed least squares (SI-LS) estimation. It is established that the proposed SI-MP provides an improvement of about 2dB in the MSE at signal-to-noise ratio (SNR) of 12dB as compared to SI-LS, for channel sparsity level of 21.5%. For BER = 10^−2, the proposed SI-MP compared to SI-LS offers a gain of about 3dB in the SNR. Moreover, our results demonstrate that an increase in the channel sparsity further enhances the performance gai

Location-aware and Superimposed-Pilot based Channel Estimation of Sparse HAP Radio Communication Channels

A superimposed (arithmetically added) Pilot (SiP) sequence based channel estimation method for beamforming assisted multi-antenna High Altitude Platform (HAP) land mobile radio communication systems is proposed, which exploits the prior available information of users' spatial location, density of users, and beam-width of HAP directional antenna. A thorough characterization of HAP sparse multipath radio propagation channels' is presented in first part of the paper, where mathematical relationship of HAP antenna beam-width with channel's delay span and optimal length of SiP base sequence are presented. Further, a location information aided and low- power SiP sequence based Stage-wise Orthogonal Match Pursuit (StOMP) algorithm is proposed for estimation of channels from single-antenna user terminals to beamforming assisted large scale multiple-antenna HAP. A thorough analysis on the basis of Normalized Channel Mean Square Error (NCMSE) and Bit Error Rate (BER) performance of proposed method is presented; where the effect of channels' sparsity level, Pilot-to-Information power Ratio (PIR), beam-width of HAP's directional antenna, amount of HAP antenna elements, density of interfering users, and spatial location of active user terminal are thoroughly studied. A comparison of the proposed method with a notable reference technique available in the literature is also presented

Estimation of Nonalcoholic Fatty Liver Disease in Patients with Normal BMI on Ultrasound

Background: Non-Alcoholic fatty liver disease is common in adults and it is increasing in patients with normal BMI in Asian countries. Non-alcoholic fatty liver disease (NAFLD) occurs not only in obese individuals but also in non-obese ones. The association between NAFLD and metabolic events in a non-obese population is also evident.. Objective: To estimate nonalcoholic fatty liver disease in patients with normal BMI on ultrasound. Methodology: Analytical Cross-sectional prospective study in which 59 patients were enrolled in the research. All the patient’s data had been composed from indoor of hospital, outdoor of hospital, DHA Medical Center, Lahore. After well-versed consent, data was composed through ultrasound machine. The data, such as patient characteristics, hypertension, impaired fasting glucose, were extracted from medical records, and statistical analysis was performed. Results: The present study is retrospective cross sectional observational study.60 patients (29males 49.2% 31 female 50.8%) were enrolled in this study. According to abdominal ultrasonography, 72.9% of patients with normal BMI were diagnosed to have Non-alcoholic fatty liver disease and identified to have fatty changes in the liver. Conclusion: In our study we estimated that nonalcoholic fatty liver disease was present in patients with normal body mass index by imaging the echotexture of liver on ultrasound. Having increased echogenicity, due to poor diet and other associated diseases such as high blood pressure, impaired fasting glucose and low HDL cholesterol patients were getting NAFLD. Keywords: Nonalcoholic fatty liver disease (NAFLD), Body Mass Index (BMI), Ultrasonography (USG). DOI: 10.7176/JHMN/92-03 Publication date:August 31st 202

Lead-Resistant Morganella morganii Rhizobacteria Reduced Lead Toxicity in Arabidopsis thaliana by Improving Growth, Physiology, and Antioxidant Activities

Biological remediation serves as a powerful technique for addressing heavy metals toxicity in metals-contaminated soils. The present study aimed to evaluate the efficacy of lead (Pb)-resistant rhizobacterial strains on growth, photosynthetic traits, and antioxidant activities of the Arabidopsis plant under lead toxicity in pot conditions. Two pre-isolated and pre-characterized Pb-resistant Morganella morganii (ABT3) and Morganella morganii (ABT9) strains were used for inoculating Arabidopsis plants grown under varying Pb concentrations (1.5 mM and 2.5 mM) using PbNO3 as the lead source. The treatments were set up in a completely randomized design with four replications. Data on growth parameters, physiological characteristics, lipid peroxidation, and antioxidant activities were recorded at harvesting. It was observed that Pb contamination caused a significant reduction in Arabidopsis growth, chlorophyll content and quantum yield at both lead concentrations. The Pb concentration of 2.5 mM, showed a substantial decrease in all parameters, including shoot fresh weight (58.72%), shoot dry weight (59.31%), root fresh weight (67.31%), root dry weight (67.28%), chlorophyll content (48.69%), quantum yield (62.36%), catalase activity (65.30%), superoxide dismutase (60.88%), and peroxidase activity (60.54%) while increasing lipid peroxidation (113.8%). However, the inoculation with Pb-resistant M. morganii strains (ABT3 and ABT9) improved plant growth, photosynthesis and antioxidant activities, while reduced the malondialdehyde content of Arabidopsis compared to control plants without inoculation. The M. morganii strain ABT9 showed a maximum increase in the shoot fresh weight (67.18%), shoot dry weight (67.96%), root fresh weight (94.04%), root dry weight (93.92%), shoot length (148.88%), root length (123.33%), chlorophyll content (52.53%), quantum yield (58.57%), catalase activity (39.46%), superoxide dismutase (21.84%), and peroxidase activity (22.34%) while decreasing lipid peroxidation (35.28%). PCA analysis further showed that all nine treatments scattered differently across the PC1 and PC2, having 81.4% and 17.0% data variance, respectively, indicating the efficiency of Pb-resistant strains. The heatmap further validated that the introduction of Pb-resistant strains positively correlated with the growth parameters, quantum yield, chlorophyll content and antioxidant activities of Arabidopsis seedlings. Both Pb-resistant strains improved Arabidopsis plant growth and photosynthetic efficiency under lead stress conditions. Thus, both Morganella morganii ABT3 and Morganella morganii ABT9 strains can be considered as bio-fertilizer for reducing lead toxicity thereby improving plant growth and physiology in metal-contaminated agricultural soils.11s

Compressed Sensing of Sparse Multipath MIMO Channels with Superimposed Training Sequence

Recent advances in multiple-input multiple-output (MIMO) systems have renewed the interests of researchers to further explore this area for addressing various dynamic challenges of emerging radio communication networks. Various measurement campaigns reported recently in the literature show that physical multipath MIMO channels exhibit sparse impulse response structure in various outdoor radio propagation environments. Therefore, a comprehensive physical description of sparse multipath MIMO channels is presented in first part of this paper. Superimposing a training sequence (low power, periodic) over the information sequence offers an improvement in the spectral efficiency by avoiding the use of dedicated time/frequency slots for the training sequence, which is unlike the traditional schemes. The main contribution of this paper includes three superimposed training (SiT) sequence based channel estimation techniques for sparse multipath MIMO channels. The proposed techniques exploit the compressed sensing theory and prior available knowledge of channel’s sparsity. The proposed sparse MIMO channel estimation techniques are named as, SiT based compressed channel sensing (SiT-CCS), SiT based hardlimit thresholding with CCS (SiT-ThCCS), and SiT training based match pursuit (SiT-MP). Bit error rate (BER) and normalized channel mean square error are used as metrics for the simulation analysis to gauge the performance of proposed techniques. A comparison of the proposed schemes with a notable first order statistics based SiT least squares (SiT-LS) estimation technique is presented to establish the improvements achieved by the proposed schemes. For sparse multipath time-invariant MIMO communication channels, it is observed that SiT-CCS, SiT-MP, and SiT-ThCCS can provide an improvement up to 2, 3.5, and 5.2 dB in the MSE at signal to noise ratio (SNR) of 12 dB when compared to SiT-LS, respectively. Moreover, for BER=10 −1.9 BER=10−1.9, the proposed SiT-CCS, SiT-MP, and SiT-ThCCS, compared to SiT-LS, can offer a gain of about 1, 2.5, and 3.5 dB in the SNR, respectively. The performance gain in MSE and BER is observed to improve with an increase in the channel sparsity

Clinical Characteristics, Racial Inequities, and Outcomes in Patients with Breast Cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) Cohort Study

BACKGROUND: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations. METHODS: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity. RESULTS: 1383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32-1.67]); Black patients (aOR 1.74; 95 CI 1.24-2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70-6.79) and Other (aOR 2.97; 95 CI 1.71-5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83-12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63-3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20-2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66-3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89-22.6]). Hispanic ethnicity, timing, and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort was 9% and 37%, respectively however, it varied according to the BC disease status. CONCLUSIONS: Using one of the largest registries on cancer and COVID-19, we identified patient and BC-related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to non-Hispanic White patients. FUNDING: This study was partly supported by National Cancer Institute grant number P30 CA068485 to Tianyi Sun, Sanjay Mishra, Benjamin French, Jeremy L Warner; P30-CA046592 to Christopher R Friese; P30 CA023100 for Rana R McKay; P30-CA054174 for Pankil K Shah and Dimpy P Shah; KL2 TR002646 for Pankil Shah and the American Cancer Society and Hope Foundation for Cancer Research (MRSG-16-152-01-CCE) and P30-CA054174 for Dimpy P Shah. REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research grant support (UL1 TR000445 from NCATS/NIH). The funding sources had no role in the writing of the manuscript or the decision to submit it for publication. CLINICAL TRIAL NUMBER: CCC19 registry is registered on ClinicalTrials.gov, NCT04354701

Massive-MIMO Sparse Uplink Channel Estimation Using Implicit Training and Compressed Sensing

Massive multiple-input multiple-output (massive-MIMO) is foreseen as a potential technology for future 5G cellular communication networks due to its substantial benefits in terms of increased spectral and energy efficiency. These advantages of massive-MIMO are a consequence of equipping the base station (BS) with quite a large number of antenna elements, thus resulting in an aggressive spatial multiplexing. In order to effectively reap the benefits of massive-MIMO, an adequate estimate of the channel impulse response (CIR) between each transmit–receive link is of utmost importance. It has been established in the literature that certain specific multipath propagation environments lead to a sparse structured CIR in spatial and/or delay domains. In this paper, implicit training and compressed sensing based CIR estimation techniques are proposed for the case of massive-MIMO sparse uplink channels. In the proposed superimposed training (SiT) based techniques, a periodic and low power training sequence is superimposed (arithmetically added) over the information sequence, thus avoiding any dedicated time/frequency slots for the training sequence. For the estimation of such massive-MIMO sparse uplink channels, two greedy pursuits based compressed sensing approaches are proposed, viz: SiT based stage-wise orthogonal matching pursuit (SiT-StOMP) and gradient pursuit (SiT-GP). In order to demonstrate the validity of proposed techniques, a performance comparison in terms of normalized mean square error (NCMSE) and bit error rate (BER) is performed with a notable SiT based least squares (SiT-LS) channel estimation technique. The effect of channels’ sparsity, training-to-information power ratio (TIR) and signal-to-noise ratio (SNR) on BER and NCMSE performance of proposed schemes is thoroughly studied. For a simulation scenario of: 4 × 64 massive-MIMO with a channel sparsity level of 80 % and signal-to-noise ratio (SNR) of 10 dB , a performance gain of 18 dB and 13 dB in terms of NCMSE over SiT-LS is observed for the proposed SiT-StOMP and SiT-GP techniques, respectively. Moreover, a performance gain of about 3 dB and 2.5 dB in SNR is achieved by the proposed SiT-StOMP and SiT-GP, respectively, for a BER of 10 − 2 , as compared to SiT-LS. This performance gain NCME and BER is observed to further increase with an increase in channels’ sparsity

SARS-CoV-2 induced urinary tract infection in an infant: a rare case

Abstract Background The incidence of SARS-CoV-2 infection in pediatric population is less than 7% that too when associated with a urinary tract infection, the presentation is very rare. There have been numerous case reports in adults and adolescent population but very few in pediatrics and none in our socioeconomic. Case presentation We present here the case of a 1-year-old boy with SARS-CoV-2 induced urinary tract infection whose urine biochemistry showed severe urinary tract infection but no hematuria. His COVID-PCR was positive. His chest radiograph showed bilateral lung infiltrates with peri-hilar lymphadenopathy. His computerized tomography scan showed infiltrates with lung fibrosis. He was admitted to the isolation ward, successfully managed, and discharged home after 5 days of in-hospital treatment. Conclusion Pediatricians and pediatric emergency physicians should be vigilant and well aware of the atypical presentation of SARS-CoV-2 infection in infants and children, as they can present with both gastrointestinal and renal manifestations. And once missed, the patient may end up with devastating complications