Disease Prediction Research Articles

From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases

The outbreak of emerging infectious diseases poses significant challenges to global public health. Accurate early forecasting is crucial for effective resource allocation and emergency response planning. This study aims to develop a comprehensive predictive model for emerging infectious diseases, integrating the blending framework, transfer learning, incremental learning, and the biological feature Rt to increase prediction accuracy and practicality. By transferring features from a COVID-19 dataset to a monkeypox dataset and introducing dynamically updated incremental learning techniques, the model's predictive capability in data-scarce scenarios was significantly improved. The research findings demonstrate that the blending framework performs exceptionally well in short-term (7-day) predictions. Furthermore, the combination of transfer learning and incremental learning techniques significantly enhanced the adaptability and precision, with a 91.41% improvement in the RMSE and an 89.13% improvement in the MAE. In particular, the inclusion of the Rt feature enabled the model to more accurately reflect the dynamics of disease spread, further improving the RMSE by 1.91% and the MAE by 2.17%. This study underscores the significant application potential of multimodel fusion and real-time data updates in infectious disease prediction, offering new theoretical perspectives and technical support. This research not only enriches the theoretical foundation of infectious disease prediction models but also provides reliable technical support for public health emergency responses. Future research should continue to explore integrating data from multiple sources and enhancing model generalization capabilities to further enhance the practicality and reliability of predictive tools.

Systematic Review on Citizen Science and Artificial Intelligence for Vector-Borne Diseases

Abstract. Vector-borne diseases (VBDs) pose a significant threat to public health globally. VBDs are a significant public health concern worldwide, with infections such as Malaria, Dengue Fever, Zika Virus, and Lyme Disease posing a threat to global health security. There is a need for innovative and effective strategies to control these diseases. One potential solution lies in the integration of citizen science and artificial intelligence technologies. Citizen science, which involves the participation of volunteers in scientific research, can greatly contribute to data collection and monitoring efforts for vector-borne diseases. Artificial intelligence can enhance the analysis of this data, leading to improved disease surveillance, prediction, and control strategies. Citizen Science involves active public participation in scientific research, data collection, and analysis, while AI and Machine Learning (ML) techniques offer powerful tools for processing and interpreting large datasets. By leveraging the power of citizen science and artificial intelligence, we can harness the collective efforts of volunteers and advanced technology to better understand, track, and mitigate the spread of vector-borne diseases. Through the combination of citizen science and artificial intelligence, a more comprehensive and efficient approach can be taken to gather data on vector-borne diseases, analyze the data, and inform public health interventions. This systematic review aims to explore the role of citizen science and artificial intelligence in addressing the challenges associated with vector-borne diseases. It will examine the existing literature on the use of citizen science and artificial intelligence in vector-borne disease research, including their applications, benefits, and limitations, in order to provide insights and recommendations for future research and public health strategies.

Resource optimization in smart electronic health systems using IoT for heart disease prediction via feedforward neural networks

Resource optimization in smart electronic health systems using IoT for heart disease prediction via feedforward neural networks

Differential evolution optimization based ensemble framework for accurate cervical cancer diagnosis

Cervical cancer remains a significant global health concern, demanding accurate and efficient diagnostic approaches for early detection and treatment. However, there are situations where a single deep learning model might not be sufficient to adequately capture the pertinent information required for precise disease prediction from intricate datasets. In this paper, we introduce an innovative ensemble learning framework that leverages the Differential Evolution (DE) algorithm to enhance the detection of cervical cancer. Our innovative approach combines the collective strength of a diverse set of base models using feature fusion techniques to achieve precise and robust classification. We have developed three base models, denoted as Fusion 1, Fusion 2, and Fusion 3, where each fusion model incorporates two pre-trained models using a feature fusion technique. To capture sequential dependencies and improve feature attention, we have integrated ConvLSTM layers and Squeeze-and-Excitation (SE) blocks into these base models. Subsequently, we've amalgamated the predictions of each base model using a weighted aggregation scheme, with the weights optimized using the DE algorithm. Our ensemble framework introduces a novel method for dynamic weight adjustment, utilizing advanced fusion techniques to improve cervical cancer detection accuracy. This approach ensures robust diagnostic performance across cancer cells datasets, enhancing patient outcomes and community health. Our evaluation covers two datasets, SIPaKMeD and Mendeley LBC, demonstrating the remarkable performance and robustness of our ensemble model. It consistently outperforms individual base models and even surpasses state-of-the-art methods, achieving an impressive accuracy rate of 98.96 % on the Mendeley LBC dataset and 95.68 % on the SIPaKMeD dataset. The incorporation of ConvLSTM layers, SE blocks, and DE optimization significantly contributes to the model's exceptional accuracy, its ability to generalize effectively, and its adaptability to diverse scenarios. Additionally, we employ Grad-CAM to visualize how the base models focus on crucial regions for accurate predictions, providing insights into the model's decision-making process. Our ensemble model exhibits outstanding Area under the Curve (AUC) values in Receiver Operating Characteristic (ROC) analysis, further confirming its effectiveness. This research represents a significant advancement in cervical cancer detection, underscoring the potential of ensemble learning techniques optimized through DE for enhancing diagnostic accuracy and advancing patient care.

An Improved Reptile Search Algorithm with Multiscale Adaptive Deep Learning Technique and Atrous Spatial Pyramid Pooling for IoT-based Smart Agriculture Management

Smart farming is an enhanced option for increasing food production, resource management and labour. Existing prediction methods need trained experts to analyse the data, which is a time-consuming process, and thus, there is a need for smart farming with the Internet of Things (IoT). Hence, a well-developed IoT-assisted smart agriculture model is proposed for managing agricultural needs to improve the economic value of farmers. The proposed framework constitutes four major aspects like Crop prediction, Crop yield prediction, Plant disease prediction and Smart irrigation. Initially, the crop images are taken as the input and fed to the Multi-scale Adaptive and Attention-based Convolution Neural Network with Atrous Spatial Pyramid Pooling (MACNN-ASPP), where the dissimilar crops are classified and obtained. In the second aspect, the crop image as well as crop-related data, are fetched from the data sources and given as input. Further, the deep features of the image are extracted that are added with soil and environment condition data. Then this feature is subjected to the Multi-scale Adaptive and Attention-based One-Dimensional Convolution Neural Network with Atrous Spatial Pyramid Pooling (MA1DCNN-ASPP) for crop yield prediction. While in the third aspect, the leaf images are assembled from the data file and fed as input to the MACNN-ASPP for detecting the variety of diseases affecting the plants. In the final aspect, smart irrigation is done by collecting field images with related data. Further, the deep features retrieved from the field images are applied to MA1DCNN-ASPP, where the different conditions of the field will be attained. In order to develop the model adaptively, the hyper-parameters in the network are optimised using the Improved Reptile Search Algorithm (IRSA). Finally, the investigation is done over the proposed methodology using multiple evaluation metrics. In contrast with other approaches, the proposed smart agriculture outperforms the performance of managing crops without any hazardous effects. Accuracy validation executed in the implemented IRSA-MACNN-ASSP-based crop yield prediction model accomplished better efficiency as 6.89%, 4.45%, 2.19% and 1.08% better than the classical techniques like CNN, LSTM, 1DCNN and MA1DCNN-ASPP.

Mechanisms of pathogenicity in the hypertrophic cardiomyopathy-associated TNNI3 c.235C > T variant

BackgroundHypertrophic cardiomyopathy (HCM) is typically manifested as a hereditary disorder, with 30 %–60 % of cases linked to cardiac sarcomere gene mutations. Despite numerous identified TNNI3 mutations associated with HCM, their severity, prevalence, and disease progression vary. The link between TNNI3 variants and phenotypes remains largely unexplored. This study aims to elucidate the impact of the TNNI3 c.235C > T mutation on HCM through clinical research and cell experiments and to explore its mechanism in HCM development. MethodsWe screened an HCM family for pathogenic gene mutations using gene sequencing. The proband and family members were assessed through electrocardiography, echocardiography, and cardiac MRI, and a pedigree map was created for disease prediction analysis. Mutant plasmids were constructed with the TNNI3 c.235C > T mutation and transfected into the AC16 human cardiomyocyte cell line to investigate the mutation's effects. ResultsThe TNNI3 c.235C > T mutation was identified as the disease-causing variant in the family. This mutation led to the upregulation of hypertrophy-associated genes ANP, BNP, and MYH7, increased cardiomyocyte size, and activation of the ERK signaling pathway. Further investigations revealed that the TNNI3 c.235C > T mutation impaired mitochondrial function, disrupted cardiomyocyte metabolism, and increased cellular autophagy and apoptosis. ConclusionsThe TNNI3 c.235C > T gene mutation may be a pathogenic factor for HCM, showing heterogeneous features and clinical phenotypes. This mutation induces myocardial hypertrophy, activates the ERK signaling pathway, and exacerbates mitochondrial dysfunction, apoptosis, and autophagy in cardiomyocytes. These findings provide insights into the mechanism of HCM caused by gene mutations and may inform HCM treatment strategies.

Retraction Note: Feature optimization by discrete weights for heart disease prediction using supervised learning

Retraction Note: Feature optimization by discrete weights for heart disease prediction using supervised learning

Early diagnosis of Alzheimer’s disease using a group self-calibrated coordinate attention network based on multimodal MRI

Convolutional neural networks (CNNs) for extracting structural information from structural magnetic resonance imaging (sMRI), combined with functional magnetic resonance imaging (fMRI) and neuropsychological features, has emerged as a pivotal tool for early diagnosis of Alzheimer’s disease (AD). However, the fixed-size convolutional kernels in CNNs have limitations in capturing global features, reducing the effectiveness of AD diagnosis. We introduced a group self-calibrated coordinate attention network (GSCANet) designed for the precise diagnosis of AD using multimodal data, including encompassing Haralick texture features, functional connectivity, and neuropsychological scores. GSCANet utilizes a parallel group self-calibrated module to enhance original spatial features, expanding the field of view and embedding spatial data into channel information through a coordinate attention module, which ensures long-term contextual interaction. In a four-classification comparison (AD vs. early MCI (EMCI) vs. late MCI (LMCI) vs. normal control (NC)), GSCANet demonstrated an accuracy of 78.70%. For the three-classification comparison (AD vs. MCI vs. NC), it achieved an accuracy of 83.33%. Moreover, our method exhibited impressive accuracies in the AD vs. NC (92.81%) and EMCI vs. LMCI (84.67%) classifications. GSCANet improves classification performance at different stages of AD by employing group self-calibrated to expand features receptive field and integrating coordinated attention to facilitate significant interactions among channels and spaces. Providing insights into AD mechanisms and showcasing scalability for various disease predictions.

Machine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention in developing countries

Background Tuberculosis (TB) remains one of the top infectious killers in the world and a prominent fatal disease in developing countries. This study proposes a prototypical solution to early prevention of TB based on its primary symptoms, signs, and risk factors, implemented by means of machine learning (ML) predictive algorithms. Further novelty of the study lies in the uniqueness of patient dataset collected from three top-ranked hospitals of Sindh, Pakistan, via a self-administered survey patient-records that comprises a set of questions asked by the doctors treating TB patients in real-time. A total of 1,200 survey patient-records were evenly distributed among all three hospitals, viz. ICT Kotri, LUMHS Jamshoro, and Civil Hospital Hyderabad. Methods To develop the required prototypes, the research made use of five distinct benchmark ML algorithms: decision tree (DT), Gaussian naive Bayes (GNB), logistic regression classifier (LRC), adaptive boosting (AdaBoost), and neural network (NN), whose performance was evaluated by considering various performance metrics, i.e., accuracy, precision, recall, F1 score, and confusion matrix. Results The experimental results, graphically visualized and systematically discoursed, demonstrate that early detection of TB classifiers, including DT, GNB, LRC, AdaBoost, and NN, attained accuracy rates of 92.11%, 89.04%, 90.35%, 93.42%, and 92.98%, respectively. These results indicate effective diagnosis of TB disease by each implemented ML algorithm.

Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022

A high-accuracy, continuous air temperature (Ta) dataset with high spatiotemporal resolution is essential for human health, disease prediction, and energy management. Existing datasets consider factors such as elevation, latitude, and surface temperature but insufficiently address meteorological and spatiotemporal factors, affecting accuracy. Additionally, no high-resolution dataset currently includes daily maximum (Tmax), minimum (Tmin), and mean (Tmean) temperatures generated using a unified methodology. Here, we introduce the four-dimensional spatiotemporal deep forest (4D-STDF) model, integrating 12 multisource factors, encompassing static and dynamic parameters, and six refined spatiotemporal factors to produce Ta datasets. This approach generates three high-accuracy Ta datasets at 1 km spatial resolution covering mainland China from 2003 to 2022. These datasets, in GeoTIFF format with WGS84 projection, comprise daily Tmax, Tmin, and Tmean. The overall RMSE are 1.49 °C, 1.53 °C, and 1.18 °C for the estimates. The 4D-STDF model can also be applied to other regions with sparse meteorological stations.

Chronic Kidney Disease (CKD) Prediction Using Stochastic Deep Radial Basis for Feature Extractiona Residual Neural Network

Chronic Kidney Disease (CKD) Prediction Using Stochastic Deep Radial Basis for Feature Extractiona Residual Neural Network

Enhancing Cardiovascular Disease Prediction with XAI Technique Using Machine Learning

Enhancing Cardiovascular Disease Prediction with XAI Technique Using Machine Learning

Smart Vision Transparency: Efficient Ocular Disease Prediction Model Using Explainable Artificial Intelligence

The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches’ challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published 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.

An efficient plant disease prediction model using darner drain fly optimization-based deep convolutional neural network

An efficient plant disease prediction model using darner drain fly optimization-based deep convolutional neural network

XAI-Augmented Voting Ensemble Models for Heart Disease Prediction: A SHAP and LIME-Based Approach.

Ensemble Learning (EL) has been used for almost ten years to classify heart diseases, but it is still difficult to grasp how the "black boxes", or non-interpretable models, behave inside. Predicting heart disease is crucial to healthcare, since it allows for prompt diagnosis and treatment of the patient's true state. Nonetheless, it is still difficult to forecast illness with any degree of accuracy. In this study, we have suggested a framework for the prediction of heart disease based on Explainable artificial intelligence (XAI)-based hybrid Ensemble Learning (EL) models, such as LightBoost and XGBoost algorithms. The main goals are to build predictive models and apply SHAP (SHapley Additive expPlanations) and LIME (Local Interpretable Model-agnostic Explanations) analysis to improve the interpretability of the models. We carefully construct our systems and test different hybrid ensemble learning algorithms to determine which model is best for heart disease prediction (HDP). The approach promotes interpretability and transparency when examining these widespread health issues. By combining hybrid Ensemble learning models with XAI, the important factors and risk signals that underpin the co-occurrence of heart disease are made visible. The accuracy, precision, and recall of such models were used to evaluate their efficacy. This study highlights how crucial it is for healthcare models to be transparent and recommends the inclusion of XAI to improve interpretability and medical decisionmaking.

Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System

ABSTRACT The integration of IoT and cloud services enhances communication and quality of life, while predictive analytics powered by AI and deep learning enables proactive healthcare. Deep learning, a subset of machine learning, efficiently analyzes vast datasets, offering rapid disease prediction. Leveraging recurrent neural networks on electronic health records improves accuracy for timely intervention and preventative care. In this manuscript, Internet of Things and Cloud Computing-based Disease Diagnosis using Optimized Improved Generative Adversarial Network in Smart Healthcare System (IOT-CC-DD-OICAN-SHS) is proposed. Initially, an Internet of Things (IoT) device collects diabetes, chronic kidney disease, and heart disease data from patients via wearable devices and intelligent sensors and then saves the patient’s large data in the cloud. These cloud data are pre-processed to turn them into a suitable format. The pre-processed dataset is sent into the Improved Generative Adversarial Network (IGAN), which reliably classifies the data as disease-free or diseased. Then, IGAN was optimized using the Flamingo Search optimization algorithm (FSOA). The proposed technique is implemented in Java using Cloud Sim and examined utilizing several performance metrics. The proposed method attains greater accuracy and specificity with lower execution time compared to existing methodologies, IoT-C-SHMS-HDP-DL, PPEDL‐MDTC and CSO-CLSTM-DD-SHS respectively.

PREDIKSI PENYAKIT JANTUNG MENGGUNAKAN METODE RANDOM FOREST DAN PENERAPAN PRINCIPAL COMPONENT ANALYSIS (PCA)

Heart disease is a significant public health issue and the leading cause of death worldwide. Risk factors such as hypertension, diabetes, obesity, sedentary lifestyle, smoking, and genetic factors contribute to the development of heart disease. This study aims to develop a heart disease prediction model using the Random Forest method. The dataset used comes from the UCI Machine Learning Repository, containing data from 1026 patients with various health features. The methods used include the stages of knowledge discovery in databases (KDD), namely data selection, preprocessing, transformation, data mining, and evaluation. The study results show that the model with 100 decision trees achieved an accuracy of 0.9823. Further evaluation using the confusion matrix and classification report indicates that the Random Forest method provides 98% accuracy, 100% precision, 96% recall, and a 98% F1-score. In conclusion, the Random Forest method is effective in predicting heart disease, with features such as thal having a significant impact on model accuracy.

Radiomics based on MRI to predict recurrent L4-5 disc herniation after percutaneous endoscopic lumbar discectomy

BackgroundIn recent years, radiomics has been shown to be an effective tool for the diagnosis and prediction of diseases. Existing evidence suggests that imaging features play a key role in predicting the recurrence of lumbar disk herniation (rLDH). Thus, this study aimed to evaluate the risk of rLDH in patients undergoing percutaneous endoscopic lumbar discectomy (PELD) using radiomics to facilitate the development of more rational surgical and perioperative management strategies.MethodThis was a retrospective case-control study involving 487 patients who underwent PELD at the L4/5 level. The rLDH and negative groups were matched using propensity score matching (PSM). A total of 1409 radiomic features were extracted from preoperative lumbar MRI images using intraclass correlation coefficient (ICC) analysis, t-test, and LASSO analysis. Afterward, 6 predictive models were constructed and evaluated using ROC curve analysis, AUC, specificity, sensitivity, confusion matrix, and 2 repeated 3-fold cross-validations. Lastly, the Shapley Additive Explanation (SHAP) analysis provided visual explanations for the models.ResultsFollowing screening and matching, 128 patients were included in both the recurrence and control groups. Moreover, 18 of the extracted radiomic features were selected for generating six models, which achieved an AUC of 0.551–0.859 for predicting rLDH. Among these models, SVM, RF, and XG Boost exhibited superior performances. Finally, cross-validation revealed that their accuracy was 0.674–0.791, 0.647–0.729, and 0.674–0.718.ConclusionRadiomics based on MRI can be used to predict the risk of rLDH, offering more comprehensive guidance for perioperative treatment by extracting imaging information that cannot be visualized with the naked eye. Meanwhile, the accuracy and generalizability of the model can be improved in the future by incorporating more data and conducting multicenter studies.

Beyond the Agatston calcium score: role of calcium density and other calcified plaque markers for cardiovascular disease prediction.

To review the current evidence and highlight future strategies regarding consideration of coronary artery calcium (CAC) density in cardiovascular disease (CVD) risk stratification. Expressed as the product of plaque area and a peak calcium density weighting factor, the Agatston method is the gold-standard for measuring CAC on noncontrast cardiac computed tomography. Over the last decade, observational data have suggested that calcium density is inversely associated with CVD events and confers additional prognostic information independent of traditional risk factors and Agatston CAC scores. Specific density measures have been assessed including peak calcium density, mean CAC density, and CAC area-density discordance. Beyond calcium density, the number of affected arteries and regional distribution of CAC which may be correlated with CAC density have also improved the predictive utility of the Agatston score. Calcium density is inversely associated with CVD risk after considering plaque area and/or volume. Calcium markers including density, vessel involvement, and regional distribution confer additional prognostic information for the prediction of incident CVD among those with prevalent subclinical atherosclerosis. A future area of study includes calcium radiomics ('calcium-omics') and whether the artificial intelligence-derived automated measurement of calcium markers beyond the Agatston score may be of value in CVD risk stratification among individuals with early to advanced subclinical atherosclerosis.