Video dataset containing video quality assessment scores obtained from standardized objective and subjective testing.

This paper presents a dataset comprising 700 video sequences encoded in the two most popular video formats (codecs) of today, H.264 and H.265 (HEVC). Six reference sequences were encoded under different quality profiles, including several bitrates and resolutions, and were affected by various packet loss rates. Subsequently, the image quality of encoded video sequences was assessed by subjective, as well as objective, evaluation. Therefore, the enclosed spreadsheet contains results of both assessment approaches in a form of MOS (Mean Opinion Score) delivered by the absolute category ranking (ACR) procedure, SSIM (Structural Similarity Index Measure) and VMAF (Video Multimethod Assessment Fusion). All assessments are available for each test sequence. This allows a comprehensive evaluation of coding efficiency under different test scenarios without the necessity of real observers or a secure laboratory environment, as recommended by the ITU (International Telecommunication Union). As there is currently no standardized mapping function between the results of subjective and objective methods, this dataset can also be used to design and verify experimental machine learning algorithms that contribute to solving the relevant research issues.

A multi-strategy fusion identification model for failure mode of reinforced concrete column.

Accurate identification of the failure modes of Reinforced Concrete (RC) columns based on the design parameters of the structural members is critical for earthquake-resistant design and safety evaluation of existing structures. Existing identification methods have some problems, such as high cost, incomplete consideration of influencing factors, and low precision or recall in identifying shear or flexural-shear failure. In this paper, the main factors for the failure modes of RC columns are first analyzed and studied. Then, the problem of class imbalance in data samples is investigated. To identify the failure modes of RC columns, oversampling of data (BSB-FMC), model ensembling (RFB-FMC), cost-sensitive learning (CSB-FMC) and a fusion model of three strategies (BSFCB-FMC) are proposed. And finally, the SHapley Additive exPlanations (SHAP) method is used to provide a better interpretation of the designed model. The results show that the developed strategies can improve the accuracy of identifying the failure modes of RC columns compared to the models using a single Artificial Neural Network (ANN), a Support Vector Machine (SVM), a Random Forest (RF), and Adaptive Boosting (AdaBoost). The overall accuracy of the developed BSFCB-FMC model reaches 97%, and the precision and recall for the three failure modes are both above 90%. The designed model provides a solution for fast, accurate and cost-effective identification of the failure modes of RC columns.

Association between oral health status and occlusal bite force in young adults.

Background/purpose: Oral health is related to general health and a person's overall well-being. The aim of the present study was to explore the association between oral health status and bite force among young adults. Materials and methods: Maximum bite force (MBF) was measured using Dental Prescale II in conjunction with a pressure-sensitive film and bite force analyzer in 40 young adults aged 20 to 40. Supragingival dental plaque was collected and cultured. Plaque weight, pH, and colony counts were assessed. The decayed, missing, and filled teeth index (DMFT) and body mass index (BMI) were recorded. Results: Bite force was negatively correlated with the number of missing teeth and the sum of missing and filled teeth. When the filled-to-remaining-teeth ratio (F/R ratio) was less than 8%, the bite force was significantly higher compared to an F/R ratio of 8-25%. Additionally, the amount of total bacteria was positively correlated with total bite force, and the quantity of Streptococcus mutans (S. mutans) along with total bacteria was positively correlated with bite force in the molar region (∗P < 0.05). The molar region predominantly contributed to bite force. Conclusion: Elevated levels of cariogenic bacteria may increase the risk of tooth loss, subsequently leading to reduced bite force. This reduction in bite force can further impact the efficiency of chewing function and, consequently, the quality of life. An F/R ratio above 8% could be easily calculated clinically and could serve as a guide to identify patients, particularly young adults, at risk of reduced bite force.

Haploidentical and matched unrelated donor allogeneic hematopoietic stem cell transplantation offer similar survival outcomes for acute leukemia.

BACKGROUND: Haploidentical hematopoietic stem cell transplantation (haplo-HSCT) has emerged as an effective approach for acute leukemia, primarily due to the inherent difficulty in finding human leukocyte antigen-matched unrelated donors (MUD). Nevertheless, it remains uncertain whether haplo-HSCT and MUD-HSCT can provide comparable outcomes in patients with acute leukemia. AIMS: This study aimed to assess the overall survival (OS) and leukemia-free survival (LFS) outcomes between the MUD-HSCT and haplo-HSCT groups. METHODS AND RESULTS: This retrospective analysis encompassed adult patients with acute leukemia undergoing the initial allo-HSCT. Among these 85 patients, we stratified 33 patients into the MUD-HSCT group and 52 to the haplo-HSCT group. The primary outcomes were OS and LFS. The median OS was not reached in the haplo-HSCT group, while it reached 29.8 months in patients undergoing MUD-HSCT (p = .211). Likewise, the median LFS periods were 52.6 months in the haplo-HSCT group and 12.7 months in the MUD-HSCT group (p = .212). Importantly, neither the OS nor LFS showed substantial differences between the MUD-HSCT and haplo-HSCT groups. Furthermore, univariate analyses revealed that haplo-HSCT did not demonstrate a significantly higher risk of worse LFS (hazard ratio [HR], 0.69; 95% confidence interval [CI], 0.38-1.25; p = .216) or OS (HR, 0.67; 95% CI, 0.36-1.26; p = .214) than MUD-HSCT. Notably, a high European Group for Blood and Marrow Transplantation risk score (HR, 1.44; 95% CI, 1.10-1.87; p = .007) and non-complete remission (HR, 2.48; 95% CI, 1.17-5.23; p = .017) were significantly correlated with worse OS. CONCLUSION: Haplo-HSCT may serve as an alternative to MUD-HSCT for the treatment of acute leukemia, offering similar survival outcomes.

Acute, long-term or non-vincristine-induced peripheral neuropathy among non-Hodgkin lymphoma survivors: Symptoms, daily activities, functional status, and quality of life.

PURPOSE: This study aimed to explore the incidence and severity of vincristine-induced peripheral neuropathy (VIPN) in non-Hodgkin lymphoma (NHL) survivors (primary aim) and its impact on daily life by comparing common cancer symptoms, functional status, and quality of life (QoL) among survivors with acute, long-term, and non-VIPN (secondary aim). METHODS: This cross-sectional study examined 144 NHL survivors. Standardized questionnaires were used to assess common cancer symptoms, functional status, and QoL with the European Organization for the Research and Treatment of Cancer - Quality of Life Questionnaire (EORTC-QLQ-C30). VIPN (Chemotherapy-Induced Peripheral Neuropathy) status was classified using EORTC-QLQ-CIPN20. A self-designed interference scale was developed to determine the impact of the VIPN on daily activities. The Kruskal-Wallis test and Spearman rank correlation were used in this study. RESULTS: Among the survivors of acute and long-term VIPN, the highest incidences and most severe symptoms were found for hand numbness and foot cramps. A significant moderate correlation was found between disturbances in daily activities and acute or long-term VIPN, including gait changes, going up or down the stairs, and imbalance-related falls. Acute and long-term VIPN survivors showed worse symptoms (fatigue, insomnia, and constipation) and lower QoL than non-VIPN survivors did. In acute VIPN, social function was significantly affected, whereas in long-term VIPN, emotional and cognitive functions were affected. CONCLUSION: Numbness and cramps should be addressed in survivors of acute and long-term VIPN. Preventing falls is recommended for NHL survivors with VIPN, and psychological support is suggested for long-term VIPN survivors.

Climate-invariant machine learning.

Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations but tend to extrapolate poorly to climate regimes that they were not trained on. To get the best of the physical and statistical worlds, we propose a framework, termed "climate-invariant" ML, incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.

Does Steal Phenomenon Exist in Multiple Neurotization?-An Experimental Rat Study.

BACKGROUND: Nerve transfers from one common donor nerve to recipient nerves with multiple target branches can yield slower and unpredictable recovery in the target nerves. Our hypothesis is that steal phenomenon exists when multiple nerve neurotization comes from one donor nerve. METHODS: In 30 Sprague-Dawley rats, the left ulnar nerve (UN) was selected as the donor nerve, and the musculocutaneous nerve (MCN) and median nerve (MN) as the recipient target nerves. The rats were separated into three groups (10 rats in each): group A, UN-to-MCN (one-target); group B, UN-to-MN (one-target); and group C, UN-to-MCN and MN (two-target). The right upper limbs were nonoperative as the control group. Outcome obtained at 20 weeks after surgery included grooming test, muscle weight, compound muscle action potential, tetanic muscle contraction force, axon counts, and retrograde labeling of the involved donor and target nerves. RESULTS: At 20 weeks after surgery, muscles innervated by neurotization resulted in significant worse outcomes than the control side. This was especially true in two-target neurotization in the parameter of muscle weight and forearm flexor muscle contraction force outcome when compared to one-target neurotization. Steal phenomenon does exist because flexor muscle contraction force was significantly worse during two-target neurotization. CONCLUSION: This study proves the existence of steal phenomenon in multiple target neurotization but does not significantly affect the functional results. Postoperative rehabilitative measures (including electrical stimulation, induction exercise) and patient compliance (ambition and persistence) are other crucial factors that hold equivalent importance to long-term successful recovery.

Focused ion beam milling and MicroED structure determination of metal-organic framework crystals.

We report new advancements in the determination and high-resolution structural analysis of beam-sensitive metal organic frameworks (MOFs) using microcrystal electron diffraction (MicroED) coupled with focused ion beam milling at cryogenic temperatures (cryo-FIB). A microcrystal of the beam-sensitive MOF, ZIF-8, was ion-beam milled in a thin lamella approximately 150 nm thick. MicroED data were collected from this thin lamella using an energy filter and a direct electron detector operating in counting mode. Using this approach, we achieved a greatly improved resolution of 0.59 Å with a minimal total exposure of only 0.64 e-/A2. These innovations not only improve model statistics but also further demonstrate that ion-beam milling is compatible with beam-sensitive materials, augmenting the capabilities of electron diffraction in MOF research.

Dinaciclib inhibits the growth of acute myeloid leukemia cells through either cell cycle-related or ERK1/STAT3/MYC pathways.

Although immature differentiation and uncontrolled proliferation of hematopoietic stem cells are thought to be the primary mechanisms of acute myeloid leukemia (AML), the pathophysiology in most cases remains unclear. Dinaciclib, a selective small molecule targeting multiple cyclin-dependent kinases (CDKs), is currently being evaluated in oncological clinical trials. Despite the proven anticancer potential of dinaciclib, the differential molecular mechanisms by which it inhibits the growth of different AML cell lines remain unclear. In the current study, we treated HL-60 and KG-1 AML cell lines with dinaciclib and investigated the potential mechanisms of dinaciclib-induced AML cell growth inhibition using flow cytometry and western blotting assays. Data from HL-60 and KG-1 AML cells were validated using human primary AML cells. The results showed that the growth inhibitory effect of dinaciclib was more sensitive in HL-60 cells (IC50: 8.46 nM) than in KG-1 cells (IC50: 14.37 nM). The protein decline in Cyclin A/B and CDK1 and cell cycle arrest in the G2/M phase were more profound in HL-60 cells, corresponding to its growth inhibition. Although the growth inhibition of KG-1 cells by dinaciclib was still pronounced, the cell cycle-associated proteins were relatively insensitive. In addition to cell cycle regulation, the activation/expression of ERK1/STAT3/MYC signaling was significantly reduced by dinaciclib in KG-1 cells compared with that in HL-60 cells. Regarding the results of primary AML cells, we observed ERK1/STAT3/MYC inhibition and cell cycle regulation in different patients. These findings suggest that the cell cycle-associated and ERK1/STAT3/MYC signaling pathways might be two distinct mechanisms by which dinaciclib inhibits AML cells, which could facilitate the development of combination therapy for AML in the future.

Clinical Features and Outcomes of Primary Breast Diffuse Large B-Cell Lymphoma: A Matched-Pair Study.

Background: The influence of the breast as the primary site on the outcome of diffuse large B-cell lymphoma (DLBCL) and further changes in therapeutic strategies remain unclear. We aimed to compare the outcomes between primary breast and non-breast DLBCL and analyze the genetic profiles of some of the study cohorts using next-generation sequencing. Methods: This matched-pair study reviewed the medical records of 19 patients with stage I and II primary breast DLBCL diagnosed between January 2005 and December 2021 on the basis of the Wiseman and Liao criteria, and we used 1:4 propensity score matching to identify patients with non-breast DLBCL as the control group. The overall response rate, progression-free survival (PFS), and overall survival (OS) were the outcome measures. Results: Patients with primary breast and non-breast DLBCL had a 5-year PFS of 72.6% and 86.9%, respectively (P = .206). These 2 groups also had comparable 5-year OS (86.9% vs 87.8%; P = .772). The breast as the primary site was not associated with inferior PFS (hazard ratio [HR]: 2.14; 95% CI: 0.66-6.96; P = .206) and OS (HR: 1.26; 95% CI: 0.27-5.93; P = .772). Conclusion: Patients with primary breast DLBCL and those with non-breast DLBCL had comparable PFS and OS under rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) or R-CHOP-like regimens. Further investigations of the mutation profile, its clinical impact, potential central nervous system relapse, and prognosis of primary breast DLBCL are required.

The efficacy of adjuvant chemotherapy for older adults with stage II/III gastric cancer: a retrospective cohort study.

BACKGROUND: Adjuvant chemotherapy is recommended as the standard treatment for patients with stage II/III resected gastric cancer. However, it is unclear whether older patients also benefit from an adjuvant chemotherapy strategy. This study aimed to investigate the clinical impact of adjuvant chemotherapy in older patients with stage II/III gastric cancer. METHODS: This retrospective, real-world study analyzed 404 patients with stage II/III gastric cancer visited at our institute between January 2009 and December 2019. The clinical characteristics and outcomes of patients aged 70 years or older who received adjuvant chemotherapy were compared with those who did not receive this type of treatment. Propensity score analysis was performed to mitigate selection bias. RESULTS: Of the 404 patients analyzed, 179 were aged 70 years or older. Fewer older patients received adjuvant chemotherapy than did younger patients (60.9% vs. 94.7%, respectively; P < 0.001). Among patients aged 70 years or older, those who received adjuvant chemotherapy had improved disease-free survival (DFS) (5-year DFS rate, 53.1% vs. 30.4%; P < 0.001) and overall survival (OS) (5-year OS rate, 68.7% vs. 52.1%; P = 0.002) compared to those who did not receive adjuvant chemotherapy. A similar survival benefit was observed in the propensity-matched cohort. Multivariate analysis showed that more advanced stage was associated with poorer OS. Receipt of adjuvant chemotherapy was independently associated with a decreased hazard of death (hazard ratio (HR), 0.37; 95% confidence intervals (CI), 0.20-0.68; P = 0.002). CONCLUSIONS: Adjuvant chemotherapy may benefit older stage II/III gastric cancer patients aged ≥ 70 years. Further prospective studies are needed to confirm these findings.

Automatic recognition of teeth and periodontal bone loss measurement in digital radiographs using deep-learning artificial intelligence.

Background/purpose: Artificial Intelligence (AI) can optimize treatment approaches in dental healthcare due to its high level of accuracy and wide range of applications. This study seeks to propose a new deep learning (DL) ensemble model based on deep Convolutional Neural Network (CNN) algorithms to predict tooth position, detect shape, detect remaining interproximal bone level, and detect radiographic bone loss (RBL) using periapical and bitewing radiographs. Materials and methods: 270 patients from January 2015 to December 2020, and all images were deidentified without private information for this study. A total of 8000 periapical radiographs with 27,964 teeth were included for our model. AI algorithms utilizing the YOLOv5 model and VIA labeling platform, including VGG-16 and U-Net architecture, were created as a novel ensemble model. Results of AI analysis were compared with clinicians' assessments. Results: DL-trained ensemble model accuracy was approximately 90% for periapical radiographs. Accuracy for tooth position detection was 88.8%, tooth shape detection 86.3%, periodontal bone level detection 92.61% and radiographic bone loss detection 97.0%. AI models were superior to mean accuracy values from 76% to 78% when detection was performed by dentists. Conclusion: The proposed DL-trained ensemble model provides a critical cornerstone for radiographic detection and a valuable adjunct to periodontal diagnosis. High accuracy and reliability indicate model's strong potential to enhance clinical professional performance and build more efficient dental health services.

Humoral and cellular responses to repeated COVID-19 exposure in multiple sclerosis patients receiving B-cell depleting therapies: a single-center, one-year, prospective study.

Multiple sclerosis patients treated with anti-CD20 therapy (aCD20-MS) are considered especially vulnerable to complications from SARS-CoV-2 infection due to severe B-cell depletion with limited viral antigen-specific immunoglobulin production. Therefore, multiple vaccine doses as part of the primary vaccination series and booster updates have been recommended for this group of immunocompromised individuals. Even though much less studied than antibody-mediated humoral responses, T-cell responses play an important role against CoV-2 infection and are induced efficiently in vaccinated aCD20-MS patients. For individuals with such decoupled adaptive immunity, an understanding of the contribution of T-cell mediated immunity is essential to better assess protection against CoV-2 infection. Here, we present results from a prospective, single-center study for the assessment of humoral and cellular immune responses induced in aCD20-MS patients (203 donors/350 samples) compared to a healthy control group (43/146) after initial exposure to CoV-2 spike antigen and subsequent re-challenges. Low rates of seroconversion and RBD-hACE2 blocking activity were observed in aCD20-MS patients, even after multiple exposures (responders after 1st exposure = 17.5%; 2nd exposure = 29.3%). Regarding cellular immunity, an increase in the number of spike-specific monofunctional IFNγ+-, IL-2+-, and polyfunctional IFNγ+/IL-2+-secreting T-cells after 2nd exposure was found most noticeably in healthy controls. Nevertheless, a persistently higher T-cell response was detected in aCD20-MS patients compared to control individuals before and after re-exposure (mean fold increase in spike-specific IFNγ+-, IL-2+-, and IFNγ+/IL-2+-T cells before re-exposure = 3.9X, 3.6X, 3.5X/P< 0.001; after = 3.2X, 1.4X, 2.2X/P = 0.002, P = 0.05, P = 0.004). Moreover, cellular responses against sublineage BA.2 of the currently circulating omicron variant were maintained, to a similar degree, in both groups (15-30% T-cell response drop compared to ancestral). Overall, these results highlight the potential for a severely impaired humoral response in aCD20-MS patients even after multiple exposures, while still generating a strong T-cell response. Evaluating both humoral and cellular responses in vaccinated or infected MS patients on B-cell depletion therapy is essential to better assess individual correlations of immune protection and has implications for the design of future vaccines and healthcare strategies.

A Scalable Federated Learning Approach for Collaborative Smart Healthcare Systems with Intermittent Clients using Medical Imaging.

The healthcare industry is one of the most vulnerable to cybercrime and privacy violations because health data is very sensitive and spread out in many places. Recent confidentiality trends and a rising number of infringements in different sectors make it crucial to implement new methods that protect data privacy while maintaining accuracy and sustainability. Moreover, the intermittent nature of remote clients with imbalanced datasets poses a significant obstacle for decentralized healthcare systems. Federated learning (FL) is a decentralized and privacy-protecting approach to deep learning and machine learning models. In this paper, we implement a scalable FL framework for interactive smart healthcare systems with intermittent clients using chest X-ray images. Remote hospitals may have imbalanced datasets with intermittent clients communicating with the FL global server. The data augmentation method is used to balance datasets for local model training. In practice, some clients may leave the training process while others join due to technical or connectivity issues. The proposed method is tested with five to eighteen clients and different testing data sizes to evaluate performance in various situations. The experiments show that the proposed FL approach produces competitive results when dealing with two distinct problems, such as intermittent clients and imbalanced data. These findings would encourage medical institutions to collaborate and use rich private data to quickly develop a powerful patient diagnostic model.

An AI-Enabled ensemble method for rainfall forecasting using Long-Short term memory.

Rainfall prediction includes forecasting the occurrence of rainfall and projecting the amount of rainfall over the modeled area. Rainfall is the result of various natural phenomena such as temperature, humidity, atmospheric pressure, and wind direction, and is therefore composed of various factors that lead to uncertainties in the prediction of the same. In this work, different machine learning and deep learning models are used to (a) predict the occurrence of rainfall, (b) project the amount of rainfall, and (c) compare the results of the different models for classification and regression purposes. The dataset used in this work for rainfall prediction contains data from 49 Australian cities over a 10-year period and contains 23 features, including location, temperature, evaporation, sunshine, wind direction, and many more. The dataset contained numerous uncertainties and anomalies that caused the prediction model to produce erroneous projections. We, therefore, used several data preprocessing techniques, including outlier removal, class balancing for classification tasks using Synthetic Minority Oversampling Technique (SMOTE), and data normalization for regression tasks using Standard Scalar, to remove these uncertainties and clean the data for more accurate predictions. Training classifiers such as XGBoost, Random Forest, Kernel SVM, and Long-Short Term Memory (LSTM) are used for the classification task, while models such as Multiple Linear Regressor, XGBoost, Polynomial Regressor, Random Forest Regressor, and LSTM are used for the regression task. The experiment results show that the proposed approach outperforms several state-of-the-art approaches with an accuracy of 92.2% for the classification task, a mean absolute error of 11.7%, and an R2 score of 76% for the regression task.

Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease.

Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends.

Graph Attention-based Curriculum Learning for Mental Healthcare Classification.

Research has examined the use of user-generated data from online media as a means of identifying and diagnosing depression as a serious mental health issue that can have a significant impact on an individual's daily life. To achieve this, researchers have examined words in personal statements to identify depression. Besides aiding in diagnosing and treating depression, this research may also provide insight into its preva- lence within society. This paper introduces a Graph Attention Network (GAT) model for the classification of depression from online media. The model is based on masked self-attention layers, which assign different weights to each node in a neighbourhood without costly matrix operations. In addition, an emotion lexicon is extended by using hypernyms to improve the performance of the model. The results of the experiment demonstrate that the GAT model outperforms other architectures, achieving a ROC of 0.98. Furthermore, the embedding of the model is used to illustrate the contribution of the activated words to each symptom and to obtain qualitative agreement from psychiatrists. This technique is used to detect depressive symptoms in online forums with an improved detection rate. This technique uses previously learned embedding to illustrate the contribution of activated words to depressive symptoms in online forums. An improvement of significant magnitude was observed in the model's performance through the use of the soft lexicon extension method, resulting in a rise of the ROC from 0.88 to 0.98. The performance was also enhanced by an increase in the vocabulary and the adoption of a graph-based curriculum. The lexicon expansion method involved the generation of additional words with similar semantic attributes, utilizing similarity metrics to reinforce lexical features. The graph-based curriculum learning was utilized to handle more challenging training samples, allowing the model to develop increasing expertise in learning complex correlations between input data and output labels.

Multivariate time-series sensor vital sign forecasting of cardiovascular and chronic respiratory diseases.

Approximately 19 million people die each year from cardiovascular and chronic respiratory diseases. As a result of the recent Covid-19 epidemic, blood pressure, cholesterol, and blood sugar levels have risen. Not only do healthcare institutions benefit from studying physiological vital signs, but individuals also benefit from being alerted to health problems in a timely manner. This study uses machine learning to categorize and predict cardiovascular and chronic respiratory diseases. By predicting a patient's health status, caregivers and medical professionals can be alerted when needed. We predicted vital signs for 180 seconds using real-world vital sign data. A person's life can be saved if caregivers react quickly and anticipate emergencies. The tree-based pipeline optimization method (TPOT) is used instead of manually adjusting machine learning classifiers. This paper focuses on optimizing classification accuracy by combining feature pre-processors and machine learning models with TPOT genetic programming making use of linear and Prophet models to predict important indicators. The TPOT tuning parameter combines predicted values with classical classification models such as Naïve Bayes, Support Vector Machines, and Random Forests. As a result of this study, we show the importance of categorizing and increasing the accuracy of predictions. The proposed model achieves its adaptive behavior by conceptually incorporating different machine learning classifiers. We compare the proposed model with several state-of-the-art algorithms using a large amount of training data. Test results at the University of Queensland using 32 patient's data showed that the proposed model outperformed existing algorithms, improving the classification of cardiovascular disease from 0.58 to 0.71 and chronic respiratory disease from 0.49 to 0.70, respectively, while minimizing the mean percent error in vital signs. Our results suggest that the Facebook Prophet prediction model in conjunction with the TPOT classification model can correctly diagnose a patient's health status based on abnormal vital signs and enables patients to receive prompt medical attention.