Disease Dataset Research Articles

Peripheral immune cell abundance differences link blood mitochondrial DNA copy number and Parkinson's disease.

Mitochondrial dysfunction plays an important role in Parkinson's disease (PD), with mitochondrial DNA copy number (mtDNA-CN) emerging as a potential marker for mitochondrial health. We investigated the links between blood mtDNA-CN and PD severity and risk using the Accelerating Medicines Partnership program for Parkinson's Disease dataset, replicating our results in the UK Biobank. Our findings reveal that reduced blood mtDNA-CN levels are associated with heightened PD risk and increased severity of motor symptoms and olfactory dysfunction. We estimated blood cell composition using complete blood cell profile when available or RNA-sequencing data as a surrogate. After adjusting for blood cell composition, the associations between mtDNA-CN and PD risk and clinical symptoms became non-significant. Bidirectional Mendelian randomization analysis also found no evidence of a direct causal relationship between blood mtDNA-CN and PD susceptibility. Hence peripheral inflammatory immune responses rather than mitochondrial dysfunction underpin these previously identified associations in PD.

Predictive Modeling and Early Detection of Parkinson's Disease Using Machine Learning

Parkinson's disease is a progressive neurodegenerative disorder that impacts millions of citizens in the USA, mainly targeting the motor system and causing debilitating symptoms such as rigidity, tremors, and bradykinesia. The diagnosis of Parkinson's disease is presently heavily dependent on clinical evaluations and neurological examinations, targeting the detection of motor dysfunction. The principal aim of this study was to use machine learning as a means for early detection and prediction of Parkinson's disease. The dataset utilized for this study was the Parkinson’s Disease Dataset, retrieved from the UCI Machine Learning Repository, which included comprehensive biomedical voice measurements from a cohort of individuals, 23 of whom are diagnosed with Parkinson’s disease and 8 who are healthy controls. This dataset included a set of features extracted from voice recordings. These include parameters like fundamental frequency (pitch), amplitude variation, jitter, shimmer, and several phonation-related measures known to reflect early vocal impairments associated with the disease in question. This research project deployed three credible and proven algorithms, namely, logistic regression, random forest, and the Support Vector Machines. Besides, this study employed a combination of metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, which are more holistic toward the model performance. According to the metric performance results of the three models, several key insights were drawn into the early detection of Parkinson's Disease. Particularly, it was clear that the Random Forest model had superior accuracy and was the most reliable in classifying positive cases and healthy patients, which could be that this model turned out to be most reliable in an early detection setting. In that respect, predictive modeling in Parkinson's Disease is a capability frontier that has the potential to make much difference in clinical decision-making. Supported by Machine Learning algorithms, clinicians can trace the minute patterns in patient data, which are not easily visible through any other diagnostic means. In such cases, early detection of Parkinson's Disorder with vocal biomarkers or motor assessment may afford healthcare professionals opportunities for early interventions that might retard the disease process.

A medical disease assisted diagnosis method based on lightweight fuzzy SZGWO-ELM neural network model.

The application of neural network model in intelligent diagnosis usually encounters challenges such as continuous adjustment of network parameters and significant cost in training the network facing numerous complex physiological data. To address this challenge, this paper introduces a fuzzy SZGWO-ELM neural network model for medical disease aid diagnosis with fuzzy membership function and ELM network to refine the improved Gray Wolf optimization algorithm. Firstly, the Z-type membership function is introduced as the inertia weight to get a balance for the grey wolf in seeking the optimal solution globally and locally and ensuring fast convergence. Secondly, the S-type membership function is utilized as the adaptive weight to flexibly adjust the grey wolf search step size to facilitate a quick approximation of the optimal solution. Finally, the improved Gray Wolf optimization algorithm is used to optimize the parameters of the ELM neural network model, termed as SZGWO-ELM. This method can eliminate the need for extensive network parameter adjustments and quickly locate the optimal solution to the problem using a lightweight neural network. The performance of the SZGWO is assessed by using metrics like convergence, mean, and standard deviation. Multiple experiments reveal that this method shows superior performance. Furthermore, five publicly accessible medical disease datasets from UCI were conducted to evaluate the performance of SZGWO-ELM network model comparing with different classify model, and the results in terms of precision, sensitivity, specificity and accuracy can achieve 99.52%, 94.14%, 99.26% and 96.08%, respectively, which illustrate that the proposed SZGWO-ELM neural network significantly enhance the model's accuracy, providing better support for doctors in disease diagnosis.

Machine Learning in Healthcare: Developing an AI Assistant Doctor for Symptom-Based Disease Prediction

The rapid advancements in data science and machine learning have revolutionized various sectors, particularly healthcare. This research introduces an innovative AI Assistant Doctor system that utilizes machine learning techniques, specifically a Support Vector Classifier (SVC), to predict potential diseases based on user-reported symptoms. The system aims to bridge the gap between symptom recognition and understanding their significance, promoting early intervention and improved health outcomes. The AI Assistant Doctor offers a comprehensive healthcare tool that goes beyond disease prediction, providing users with detailed information about illnesses, preventive measures, drug recommendations, nutritional guidance, and personalized exercise suggestions. The SVC model is trained on a diverse dataset of symptoms and diseases, enabling accurate predictions. The system is designed to be accessible through a user-friendly web interface, allowing individuals to input their symptoms and receive immediate insights. In addition to disease prediction, the AI Assistant Doctor equips users with a thorough understanding of the predicted condition, including its symptoms, causes, and typical progression. It also offers preventive strategies, suggested medications, dietary advice, and customized physical activity recommendations to help users manage their health effectively. By leveraging machine learning algorithms and web-based frameworks, this research presents a cutting-edge approach to disease prediction and healthcare management, addressing the growing need for accessible medical information and timely interventions. The AI Assistant Doctor has the potential to alleviate pressure on healthcare systems, improve individual health outcomes, and support medical practitioners in delivering precise diagnoses and personalized treatment plans. Keywords: Machine learning, Disease Prediction, Support Vector Classifier (SVC), Healthcare, Symptoms, Prevention Personalized recommendations.

Hyperparameter Tuning Decision Tree and Recursive Feature Elimination Technique for Improved Chronic Kidney Disease Classification

Purpose: This study has the purpose of classifying patients with chronic kidney disease based on specific features and improving the classification models by tuning hyperparameters. This study aims to detect chronic kidney disease at an early stage. Methods: In this study, a machine learning classifier in the form of a decision tree is used to classify chronic kidney disease on the Risk Factor Prediction of Chronic Kidney Disease dataset. After that, the performance of the classifier model is improved by using feature selection, namely Recursive Feature Elimination and Hyperparameter tuning with GridSearchCV. Result: After tests were conducted 3 times namely testing with Decision Tree, Recursive Feature Elimination, and Hyperparameter tuning GridSearchCV which is the proposed method, then compared to other tests. The results from this study is using that method can improve the Decision Tree classifier in classifying chronic kidney disease patients. Novelty: Dataset that have been used in this study is from UCI machine learning repository namely Risk Factor Prediction of Chronic Kidney Disease that have 202 instances and 28 feature and after being processess and conducting test, Recursive Feature Elimination and Hyperparameter tuning GridSearchCV can improve the Decision Tree classifier in classifying chronic kidney disease.

Novel framework of significant risk factor identification and cardiovascular disease prediction

Cardiovascular disease (CVD) remains a major public health concern, characterized by high mortality rates and complex diagnostic challenges. Risk factor-based prediction models are commonly employed, but existing approaches often treat all factors equally important. This study proposes a novel method for predicting CVD using a minimal set of significant risk factors, without compromising predictive accuracy. The proposed framework consists of two main components: the Heart Disease Assessment and Review Technique (HEART) and the Stacked Meta Neural Network (SMNN). HEART has been introduced to identify the key risk factors of CVD. Different statistical techniques such as McFadden’s pseudo-R-squared, mutual information, Friedman’s and McNemar’s tests, Shapley and Odds ratio analysis were used to signify and prioritize key risk factors over non-key risk factors. SMNN with 5-fold cross-validation uses these significant risk factors to predict CVD. The method is evaluated using multiple benchmark datasets, yielding an average accuracy of 90.5% ± 1.8% (IEEE Data-port), 88.5% ± 3.3% (Government College University, Faisalabad, Pakistan) and 80.3% ± 1.3% (South African Heart Disease dataset) underscoring its robustness. This study offers new insights into CVD risk factor assessment, enhancing diagnostic accuracy and informing more effective clinical interventions to improve patient outcomes.

Burden and causes of anemia in Vietnam: insights from the global burden of disease data

BackgroundAnemia is a significant global health issue, impacting 1.92 billion people and ranking as a leading cause of disability. It is marked by low hemoglobin levels, which hinder oxygen delivery to vital organs and result from causes such as nutritional deficiencies and chronic diseases. Despite some progress, anemia remains a major concern in Southeast Asia, including Vietnam. Although the country has reduced its anemia rates, the progress is insufficient to achieve the WHO’s 2030 targets for anemia reduction among women of reproductive age. This study explores anemia trends and socio-economic determinants in Vietnam, with a focus on age and gender differences, to inform targeted interventions and policy development.MethodsThis study used the Global Burden of Diseases, Injuries, and Risk Factors (GBD) dataset to explore the burden of anemia and its causes in Vietnam from 1990 to 2021. The GBD study provides data on anemia prevalence and years lived with disability (YLD) by age, sex, and 37 causes across 204 countries and territories. Further, this study employed a regression model to examine how the anemia burden (i.e., prevalence rate and YLD rate) relates to socio-economic factors such as the Human Development Index (HDI), gender disparities in human development achievement, and gender-based inequalities in reproductive health, empowerment, and economic status in Vietnam over this period.ResultsIn 1990, there were 16.30 million anemia cases in Vietnam, decreasing slightly to 16.05 million by 2021. Male prevalence dropped from 34.90 to 26.58%, while female prevalence increased from 65.10 to 73.42%. Iron deficiency was the main cause, affecting 68.35% of cases in 1990 and 68.84% in 2021, with 24.96% in males and 75.04% in females in 2021. While anemia has decreased among infants, young children, and adolescents due to improved healthcare, it has increased among middle-aged and older adults, especially females. Regression analysis shows that higher human development and gender equality reduce anemia prevalence and disability, whereas gender inequality worsens the issue.ConclusionsThe study underscores that anemia remains a major health concern in Vietnam, marked by significant gender and age disparities. Despite a decrease in overall anemia rates and the effectiveness of specific interventions like iron-folic acid supplementation, females, particularly those of reproductive age and older adults, continue to face higher rates. Socio-economic improvements, including higher income and better human development, are linked to lower anemia rates, though the impact differs by gender. The persistent gender disparity and the shift in burden to older adults highlight the need for more comprehensive, gender-sensitive interventions. Future efforts should integrate nutritional support with broader socio-economic enhancements to effectively address anemia and improve public health in Vietnam.

Predicting the risk of chronic kidney disease based on uric acid concentration in stones using biosensors integrated with a deep learning-based ANN system

Elevated levels of uric acid (UA) in the body may not only lead to the formation of stones but also increase the risk of developing chronic kidney disease (CKD). This study presents a biosensor for detecting UA concentration in stones and a deep learning-based artificial neural network (ANN) system for analyzing CKD risk. The biosensor is a screen-printed electrode (SPE) chip, whose surface was modified using oxygen plasma, enabling the detection of UA concentration via cyclic voltammetry. Experimental results show a good linear relationship between UA concentration and anodic peak current within the range of 0.15–5 mM. The surface modification method for this biosensor is simple and cost-effective. The ANN system took age and creatinine values as inputs, utilizing the Chronic_Kidney_Disease dataset and supplementary data from literatures for training. After detecting the UA concentration in stones using the biosensor, the result was converted into serum uric acid concentration, allowing the estimation of creatinine level, which was then used by the ANN to assess the risk of developing CKD. This system can assist urologists in determining whether patients should seek consultation with nephrologists for early diagnosis and treatment.

Enhancing Heart Disease Prediction through Machine Learning: A Comparative Analysis of Algorithm Generalization across Datasets with Various Distributions

Heart disease, due to its high prevalence and mortality, remains a key area of global research. Although traditional diagnostic methods are effective, they are often invasive and time-consuming, highlighting the need for non-invasive, AI-based approaches. A significant challenge in real-world applications is ensuring model generalization across different datasets, particularly when the datasets are small. In this study, the performance of machine learning models, including Decision Tree, Random Forest, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), was evaluated on two distinct heart disease datasets with different distributions and relatively small sizes. Two datasets with varying distributions were used for training and testing, with the primary focus on assessing model generalization in crossdataset applications. It is shown in the results that, while the Decision Tree model performed best after hyperparameter tuning, the improvements in Random Forest and MLP were limited, and SVM exhibited a decline in performance after tuning in the cross-dataset task. It was found that grid search tuning has limitations in cross-dataset scenarios, especially with small datasets, where complex models are prone to overfitting. The study demonstrates that, with smaller datasets, simpler models like Decision Trees often adapt better to different datasets. Furthermore, transfer learning and domain adaptation techniques are suggested as crucial for improving model generalization. Future research should focus on employing these techniques to enhance the robustness and accuracy of heart disease prediction models across diverse datasets.

Inferring diagnoses from prescription data: a machine-learning approach

Abstract Estimating disease prevalence is a fundamental while challenging public-health goal: in the absence of solid diagnostic data, it typically requires expensive and time intensive studies. Analyzing existing medical records is an alternative, complementary approach. In particular, prescription records offer a glimpse into the health status of populations, prescribing patterns, and operational dynamics. We asked whether these could be used to infer diagnostics and, therefore, estimate disease prevalence. We used the Portuguese electronic medical prescriptions dataset (ADD NUMBERS) and trained a machine learning model that captures the probability that specific drugs are prescribed for the same or related medical conditions. We 1) constructed a manually-curated dataset of diseases and associated medications; 2) used this dataset to train embeddings models; 3) tested these models against human-classified data; 4) used the trained/tested model to uncover comorbidities associated with a) antibiotic prescription (to validate the model), and b) the impact of COVID-19 in prescription patterns of chronic-diseases. The model was able to accurately identify drugs used for targeting ten specific diseases, with few false positives and false negatives. Our analysis showed that the COVID-19 pandemic had almost no effect on the number of prescriptions for some chronic diseases (such as diabetes and depression), but, importantly, had a strong impact on the number of new diagnoses, with new users of diabetes medication in 2019 dropping by more than 16.8% in 2020. Medical prescriptions offer a promising tool to quickly assess disease prevalence, underdiagnosis, and allow a broad overview of the medical status of the population, especially in the face of health emergencies that strongly pressure the medical system. Key messages • We propose a new approach to infer diagnostics from prescription data with several promising applications. • We propose a new approach to infer diagnostics from prescription data with several promising applications.

A Non-Invasive Gait-Based Screening Approach for Parkinson’s Disease

Parkinson's disease (PD) presents a significant global health challenge, characterized by the progressive degeneration of dopamine-producing neurons in the brain, resulting in both motor and non-motor symptoms that severely impact quality of life. This study addresses the complexities of PD, highlighting the critical need for early diagnosis to slow disease progression. This research addresses the challenges of early diagnosis, such as the use of unreliable diagnostic techniques and limited healthcare resources. It uses the MMU Parkinson Disease Dataset and applies camera-based data collection to analyze gait patterns that can identify a risk of Parkinson's Disease. The study utilizes computer vision and the AlphaPose framework to analyze video data and detect body key points. By employing machine learning algorithms, including Support Vector Machines (SVM) and CatBoost, showing highly effective in identifying temporal dependencies in gait patterns. The algorithms achieved a high accuracy of 83.33% on the MMU dataset. This method enhances the accuracy of PD detection and enables immediate detection and control of the disease. The combination of advanced data analysis methods and medical knowledge offers new possibilities to develop targeted treatments that improve patient outcomes, demonstrating the potential of machine learning in effectively managing and treating Parkinson's disease. To enhance the generalizability of models, future research should collect extensive and diverse datasets covering various backgrounds and different stages of Parkinson's disease and utilize advanced techniques for extracting features to improve the accuracy of gait analysis.

Heart disease prediction using machine learning models

Abstract. Heart disease remains a global health threat, making rapid identification crucial. Using the Heart Disease Dataset from Kaggle, this research employs a Random Forest model to analyze 13 clinical variables from 1,025 samples. To improve accuracy and address class imbalance, the dataset was split into training and test sets, utilizing methods such as Z-scores, SMOTE, and feature selection. The Random Forest model, which combines multiple decision trees, achieved high performance with an accuracy of 98.54%, identifying key predictors such as chest pain type, maximum heart rate, and thalassemia. Compared to a single decision tree, the Random Forest model reduces overfitting, improves generalization, and increases predictive accuracy. Factors like cholesterol levels, resting blood pressure, and exercise-induced angina were also considered. By averaging results from multiple trees, the model offers reliable and stable predictions, highlighting its potential in clinical settings for early detection and personalized treatment strategies. This study aims to assist healthcare providers in better allocating resources, planning preventive measures, and tailoring treatment plans to individual patients.

Bio-inspired disease prediction: harnessing the power of electric eel foraging optimization algorithm with machine learning for heart disease prediction

Heart disease is the most significant health problem around the world. Thus, it emphasizes the need for accurate and efficient predictive models for early diagnosis. This study proposes an innovative approach integrating the Electric Eel Foraging Optimization Algorithm (EEFOA) with the Random Forest (RF) algorithm for classifying heart disease prediction. EEFOA draws inspiration from the foraging behaviour of electric eels, a bio-inspired optimization framework capable of effectively exploring complex solutions. The objective is to improve the predictive performance of heart disease diagnosis by integrating optimization and Machine learning methodologies. The experiment uses a heart disease dataset comprising clinical and demographic features of at-risk individuals. Subsequently, EEFOA was applied to optimize the features of the dataset and classification using the RF algorithm, thereby enhancing its predictive performance. The results demonstrate that the Electric Eel Foraging Optimization Algorithm Random Forest (EEFOARF) model outperforms traditional RF and other state-of-the-art classifiers in terms of predictive accuracy, sensitivity, specificity, precision, and Log_Loss, achieving remarkable scores of 96.59%, 95.15%, 98.04%, 98%, and 0.1179, respectively. The proposed methodology has the potential to make a significant contribution, thereby reducing morbidity and mortality rates.

An Improved Multi-Scale Feature Extraction Network for Rice Disease and Pest Recognition

In the process of rice production, rice pests are one of the main factors that cause rice yield reduction. To implement prevention and control measures, it is necessary to accurately identify the types of rice pests and diseases. However, the application of image recognition technologies focused on the agricultural field, especially in the field of rice disease and pest identification, is relatively limited. Existing research on rice diseases and pests has problems such as single data types, low data volume, and low recognition accuracy. Therefore, we constructed the rice pest and disease dataset (RPDD), which was expanded through data enhancement methods. Then, based on the ResNet structure and the convolutional attention mechanism module, we proposed a Lightweight Multi-scale Feature Extraction Network (LMN) to extract multi-scale features at a finer granularity. The proposed LMN model achieved an average classification accuracy of 95.38% and an F1-Score of 94.5% on the RPDD. The parameter size of the model is 1.4 M, and the FLOPs is 1.65 G. The results suggest that the LMN model performs rice disease and pest classification tasks more effectively than the baseline ResNet model by significantly reducing the model size and improving accuracy.

SpliceTransformer predicts tissue-specific splicing linked to human diseases

We present SpliceTransformer (SpTransformer), a deep-learning framework that predicts tissue-specific RNA splicing alterations linked to human diseases based on genomic sequence. SpTransformer outperforms all previous methods on splicing prediction. Application to approximately 1.3 million genetic variants in the ClinVar database reveals that splicing alterations account for 60% of intronic and synonymous pathogenic mutations, and occur at different frequencies across tissue types. Importantly, tissue-specific splicing alterations match their clinical manifestations independent of gene expression variation. We validate the enrichment in three brain disease datasets involving over 164,000 individuals. Additionally, we identify single nucleotide variations that cause brain-specific splicing alterations, and find disease-associated genes harboring these single nucleotide variations with distinct expression patterns involved in diverse biological processes. Finally, SpTransformer analysis of whole exon sequencing data from blood samples of patients with diabetic nephropathy predicts kidney-specific RNA splicing alterations with 83% accuracy, demonstrating the potential to infer disease-causing tissue-specific splicing events. SpTransformer provides a powerful tool to guide biological and clinical interpretations of human diseases.

Worldwide prevalence of chagas cardiomyopathy-an analysis from the global burden of disease dataset.

The worldwide prevalence of Chagas Cardiomyopathy (CCM) as well as the trends in the prevalence of CCM over time have not been well characterized. An analysis of the Global Burden of Disease (GBD) data from 1990 to 2019 was conducted to assess the burden of CCM. This study focused on determining the prevalence of CCM, along with its age-standardized prevalence rate (ASR) per 1,00,000 people, considering various patient demographics and geographical regions as defined in the GBD. Additionally, the study examined the temporal trends over this 30-year period by calculating the estimated annual percentage change (EAPC) in CCM prevalence for the global population and specific subgroups. Worldwide, the GBD reported 220,166 individuals with CCM in 1990 and 2,83,236 individuals in 2019, with a decline in the ASR from 5.23 (3.34-7.47) to 3.42 (2.2-4.91) per 1,00,000 individuals during that period. In 2019, the prevalence was highest in individuals over age 70 and in males compared to females. Among available geographic classifications in 2019, Latin American regions had the highest rates (ASR of 39.49-61.15/1,00,000), while high income North American and Western European regions had the lowest rates (ASRs of 0.67 and 0.34/1,00,000, respectively). Between 1990 and 2019, the worldwide prevalence of CCM per 1,00,000 decreased (EAPC of -0.35, -0.37 to -0.32), with similar trends among most regions and subgroups. This analysis of the GBD data reveals both global and country-specific patterns in the prevalence and trends of CCM. Notably, CCM shows the highest prevalence in Latin American countries, although it's also significantly present in regions beyond Latin America. Notably, the global age-standardized rate of CCM is on the decline, suggesting improvements in healthcare strategies or lifestyle changes across the world.

Integrin alpha1 beta1 promotes interstitial fibrosis in a mouse model of polycystic kidney disease.

Fibrosis is the cause of end-stage kidney failure in patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). The molecular and cellular mechanisms involved in fibrosis are complex and anti-fibrotic therapies have so far failed to make an impact on patient welfare. Using unbiased proteomics analysis on the Pkd1 nl/nl mouse, we found that expression of the integrin α1 subunit is increased in this model of ADPKD. In human ADPKD tissue and two single cell RNA kidney disease datasets, ITGA1 was also upregulated. To investigate the functional role of this integrin subunit in ADPKD, we generated a Pkd1 nl/nl Itga1 -/- mouse. We observed a significant reduction in kidney volume and kidney dysfunction in mice lacking the integrin α1 subunit. Kidneys from Pkd1 nl/nl Itga1 -/- mice had smaller cysts and reduced interstitial expansion and tubular atrophy. Picrosirius red staining identified a restriction in collagen staining in the interstitium and the myofibroblast marker α smooth muscle actin was also downregulated. Myofibroblast cell proliferation was reduced in Pkd1 nl/nl Itga1 -/- mice and primary fibroblast cultures demonstrated an abrogated fibrogenic phenotype in integrin α1-depleted fibroblasts. These results highlight a previously unrecognised role for the integrin α1 subunit in kidney fibrosis.

Annotating full-scan MS data using tandem MS libraries.

Full-scan mass spectrometry (MS) data from both liquid chromatography (LC) and MS imaging capture multiple ion forms, including their in-source fragments. Here we leverage such fragments to structurally annotate full-scan data from LC-MS or MS imaging by matching against peak intensity scaled tandem MS spectral libraries using precursor-tolerant reverse match scoring. Applied to inflammatory bowel disease and imaging datasets, we show the approach facilitates re-analyses of data in public repositories.

Image segmentation for pest detection of crop leaves by improvement of regional convolutional neural network

Pest detection is important for crop cultivation. Crop leaf is the main place of pest invasion. Current technologies to detect crop pests have constraints, such as low efficiency, storage demands and limited precision. Image segmentation is a fast and efficient computer-aided detection technology. High resolution image capture solidly supports the crucial processes in discerning pests from images. Study of analytical methods help parse information in the images. In this paper, a regional convolutional neural network (R-CNN) architecture is designed in combination with the radial bisymmetric divergence (RBD) method for enhancing the efficiency of image segmentation. As a special application of RBD, the hierarchical mask (HM) is produced to endorse detection and classification of the leaf-dwelling pests, offering enhanced efficiency and reduced storage requirements. Moreover, to deal with some mislabeled data, a threshold variable is introduced to adjust a fault-tolerant mechanism into HM, to generate a novel threshold-based hierarchical mask (TbHM). Consequently, the hierarchical mask R-CNN (HM-R-CNN) model and the threshold-based hierarchical mask R-CNN (TbHM-R-CNN) model are established to detect various types of healthy and pest-invasive crop leaves to select the regional image features that are rich in pest information. Then simple linear iterative clustering (SLIC) method is incorporation to finish the image segmentation for the classification of pest invasion. The models are tuned and optimized, then validated. The most optimized modeling results are from the TbHM-R-CNN model, with the classification accuracy of 96.2%, the recall of 97.5% and the F1 score of 0.982. Additionally, the HM-R-CNN model observed appreciable results second only to the best model. These results indicate that the proposed methodologies are well-suited for training and testing a dataset of plant diseases, offering heightened accuracy in pest classification. This study revealed that the proposed methods significantly outperform the existing techniques, marking a substantial improvement over current methods.

Enhancing Heart Disease Prediction with Federated Learning and Blockchain Integration

Federated learning offers a framework for developing local models across institutions while safeguarding sensitive data. This paper introduces a novel approach for heart disease prediction using the TabNet model, which combines the strengths of tree-based models and deep neural networks. Our study utilizes the Comprehensive Heart Disease and UCI Heart Disease datasets, leveraging TabNet’s architecture to enhance data handling in federated environments. Horizontal federated learning was implemented using the federated averaging algorithm to securely aggregate model updates across participants. Blockchain technology was integrated to enhance transparency and accountability, with smart contracts automating governance. The experimental results demonstrate that TabNet achieved the highest balanced metrics score of 1.594 after 50 epochs, with an accuracy of 0.822 and an epsilon value of 6.855, effectively balancing privacy and performance. The model also demonstrated strong accuracy with only 10 iterations on aggregated data, highlighting the benefits of multi-source data integration. This work presents a scalable, privacy-preserving solution for heart disease prediction, combining TabNet and blockchain to address key healthcare challenges while ensuring data integrity.