Disease Prediction Research Articles

Retraction Note: Parkinson’s disease prediction and drug personalization using machine learning techniques

Retraction Note: Parkinson’s disease prediction and drug personalization using machine learning techniques

Heart disease prediction using KNN algorithm approach

Machine getting to know and records-based totally techniques for predicting and diagnosing coronary heart ailment may be an extraordinary medical gain, but it's far a chief undertaking to improve. In many countries there may be a shortage of cardiovascular professionals and a giant quantity of instances of misdiagnosis may be addressed by way of setting up correct and powerful early-stage cardiac forecasts with the aid of medical decision-making analysis of virtual patient information. This has a look at aimed picking out excessive-overall performance devices getting to know variants for such diagnostic purposes. Several gadget-getting-to-know algorithms have been used which might be in comparison and done with accuracy and accuracy in predicting heart disease. The scores of the importance of each detail are restrained to all algorithms used except MLP and KNN. All elements are calculated based totally on the cost points to find those who provide high-danger coronary heart disease prognosis. The look discovered that using Kaggle's 3-segment cardiac database based on pro-k (KNN), choice tree (DT), and random Forests (RF) RF technique algorithms done ninety-seven.2% accuracy and 97.2% sensitivity to make clear. Therefore, we've observed that an easily supervised machine gaining knowledge of a set of rules can be wielded to make coronary heart disease conjecture with the very best accuracy and the most satisfactory possible use.

Retraction Note: Learning-based techniques for heart disease prediction: a survey of models and performance metrics

Retraction Note: Learning-based techniques for heart disease prediction: a survey of models and performance metrics

Role of bacterial pathogens in microbial ecological networks in hydroponic plants.

Plant-associated microbial communities are crucial for plant growth and health. However, assembly mechanisms of microbial communities and microbial interaction patterns remain elusive across vary degrees of pathogen-induced diseases. By using 16S rRNA high-throughput sequencing technology, we investigated the impact of wildfire disease on the microbial composition and interaction network in plant three different compartments. The results showed that pathogen infection significantly affect the phyllosphere and rhizosphere microbial community. We found that the primary sources of microbial communities in healthy and mildly infected plants were from the phyllosphere and hydroponic solution community. Mutual exchanges between phyllosphere and rhizosphere communities were observed, but microbial species migration from the leaf to the root was rarely observed in severely infected plants. Moreover, wildfire disease reduced the diversity and network complexity of plant microbial communities. Interactions among pathogenic bacterial members suggested that Caulobacter and Bosea might be crucial "pathogen antagonists" inhibiting the spread of wildfire disease. Our study provides deep insights into plant pathoecology, which is helpful for the development of novel strategies for phyllosphere disease prediction or prevention.

The Role of Mathematics in Machine Learning for Disease Prediction: An In-Depth Review in the Healthcare Domain

The rapid advancements in healthcare technologies and the increasing complexity of medical data have made it imperative to explore and optimize predictive models for disease management. This study aims to conduct a systematic literature review to identify advancements, challenges, and opportunities in disease prediction using machine learning (ML) within the healthcare domain. The literature sources include Scopus, DOAJ, and Google Scholar, covering the period from 2013 to 2024. The findings reveal that both machine learning (ML) and deep learning (DL) algorithms have significant potential for disease prediction and treatment outcomes in various clinical contexts. Algorithms such as Random Forest, Logistic Regression, and ensemble techniques like Boosting have demonstrated strong performance in numerous studies. However, the effectiveness of these algorithms is highly context-dependent, including the type of disease, patient characteristics, and available data. Deep learning, particularly Convolutional Neural Networks (CNNs) and hybrid Long Short-Term Memory (LSTM) models, excels in handling complex, high-dimensional data, providing higher prediction accuracy compared to traditional ML models. This research shows that deep learning models, especially CNN and hybrid LSTM, achieve higher accuracy in disease prediction compared to traditional ML models. However, challenges related to data quality, privacy, and the underlying mathematical modeling of these algorithms remain to be overcome for wider applications.

Phonocardiogram Signal Analysis Based Premature Cardiac Influence Detection using Resnet50 CNN for Public Health Protection

Phonocardiogram (PCG) signal analysis plays a crucial role in the early detection of cardiac abnormalities, which is essential for public health protection. The premature disease prediction is tedious because the feature analysis takes place dimensionality problems which leads poor accuracy. The prevailing ensemble learning models’ methods doesn’t concentrate disease margin facts and properties during classification produce higher false rate because for adjusting feature margins gets lower accuracy. To resolve this problem, we propose a efficient Fuzzy c-means feature modality based Resnet50-RNN to detect premature cardiac influence detection for improving public health. The research proposes a method for premature cardiac influence detection using a ResNet50 Convolutional Neural Network (CNN) model for early prediction of heart disease. To address the issue of imbalanced data, Synthetic Minority Oversampling Technique (SMOTE) is employed for preprocessing. The Cardiac Disease Influence Rate (CDIR) is then used to identify the maximum deficiency-affected feature margins. Fuzzy c-means is utilized to select the mutual features associated with the heart disease influence rate. The ResNet50 CNN model is employed for classification, enabling the prediction of disease margins by risk category. The proposed system effectively identifies the disease based on the disease-affected scaling margin, leading to early disease impact prediction with higher accuracy, specificity, F1-measure, and lower false rates compared to other existing systems. The implementation also demonstrates reduced time complexity.

Enhancing Health Data Assessment Performance: A Multi-Sensor Focused Study for Public Health

The term health data assessment performance described the process of assessing efficacy of acquiring and reporting health data. Multi sensor approach for predicting diabetes in public health scenarios, multi sensor for public health assimilates assorted data source and addresses community health challenges widely and efficiently. In this study, we proposed a Horse Herd Fused Discriminant Generative Adversarial Network (HHF-DGAN) algorithm for diabetes prediction. Sensor-based diabetes data is gathered from kaggle, and then data preprocessing using z-score normalization is inaccurate records from a dataset. To extract feature from the pre-processed data, Short-Time Fourier Transform (STFT) is used for dimensionality reduction. HHF-DGAN is a public health technique that addresses diabetes prevention and management while also including multi sensor assessment data. The proposed methods are to evaluate F1-score (86%), precision (92%), recall (93%), and accuracy (93%). The effectiveness of the suggested strategy in improving prediction reliability for diabetes detection in public health circumstances has been established by comparing it with existing methods. The study concludes field of healthcare assessments with a robust framework for leveraging multi sensor data and deep learning methods in disease prediction.

Effective and efficient automatic detection, prediction and prescription of potential disease in berry family

Effective and efficient automatic detection, prediction and prescription of potential disease in berry family

Significance of Machine Learning Algorithms to Improve Predictive Analytics in Chronic Disease Management through Pharmacogenomics

The treatment of chronic diseases is an important concern in global health. Machine learning (ML)-based disease prediction models are becoming more important for making informed medical decisions in light of the paradigm shift towards preventative care. Integrating genetic, pharmacogenomic, personal health, and psychosocial data can greatly assist healthcare practitioners in making treatment-related decisions for patients with chronic diseases. This study utilizes a Deep Convolutional Neural Network-assisted Chronic Disease Management (DCNN-CDM) through pharmacogenomics and an improved predictive analytics model to enable informed real-time decision-making at the point of care. Data augmentation in terms of feature space allows the DCNN model to avoid over-fitting while effectively capturing high-level features submerged in chronic disease datasets. Afterwards, this work suggests an attention-empowered DCNN model to enhance sick case diagnosis accuracy, which augments data regarding sample space, thereby alleviating the class imbalance issue. Electronic health information data mining is now using predictive analytics to determine individuals at risk of acquiring chronic disease problems. The suggested model may aid in the early and accurate diagnosis of chronic diseases. The numerical outcomes demonstrate that the recommended DCNN-CDM model increases the accuracy rate of 98.7%, patient monitoring rate of 97.5%, F1-score rate of 96.3% and predictive performance rate of 95.1% compared to other existing methodologies.

Handling imbalanced medical datasets: review of a decade of research

Machine learning and medical diagnostic studies often struggle with the issue of class imbalance in medical datasets, complicating accurate disease prediction and undermining diagnostic tools. Despite ongoing research efforts, specific characteristics of medical data frequently remain overlooked. This article comprehensively reviews advances in addressing imbalanced medical datasets over the past decade, offering a novel classification of approaches into preprocessing, learning levels, and combined techniques. We present a detailed evaluation of the medical datasets and metrics used, synthesizing the outcomes of previous research to reflect on the effectiveness of the methodologies despite methodological constraints. Our review identifies key research trends and offers speculative insights and research trajectories to enhance diagnostic performance. Additionally, we establish a consensus on best practices to mitigate persistent methodological issues, assisting the development of generalizable, reliable, and consistent results in medical diagnostics.

Design of Epidemic Model For Covid-19 Disease Prediction Using Deep Learning

Promising technologies are available in the developing field of Public Health Surveillance (PHS) to help public health authorities make decisions more quickly by expediting the process of monitoring, analysing, and using unofficial sources. The cornerstone of public health practice is public health surveillance. Influencing policy decisions, spearheading new program initiatives, improving public relations, and helping organisations assess their research expenditures all depend on surveillance data. Public health experts may find that mathematical models are an effective tool in controlling epidemics, which might result in a significant drop in the number of cases and deaths. Moreover, decision-makers can optimise prospective control strategies, including as vaccination campaigns, lockdowns, and containment measures, by using mathematical models to produce long- and short-term forecasts. This work suggests the evolution of epidemics.

Artificial Intelligence in Advance Angiogenesis and Inflammation Research: A Breakthrough in Disease Prediction and Therapy

Background: Angiogenesis and inflammation are fundamental biological processes, crucial to human health. Dysregulation of these processes is implicated in diseases such as cancer, cardiovascular disorders, and autoimmune diseases. Despite advances in research, the complexity of these interactions has been challenging to understand. Recent developments in artificial intelligence (AI) offer a promising approach for overcoming these challenges, especially in big data analysis. This study explores AI applications in quantifying angiogenesis and inflammatory markers and predicting disease progression. Methods: AI algorithms, including machine learning (ML) and deep learning (DL), were employed to analyze high-throughput biological data. The study applied Lasso regression for biomarker discovery, Long Short-Term Memory (LSTM) networks for predicting disease progression, and Gaussian Mixture Models (GMM) for patient subgroup identification. Image analysis using DeepLabv3+ was conducted to assess angiogenesis and inflammatory markers in histological images. Model performance was evaluated using R-squared (R²), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) metrics. Results: The AI framework demonstrated high accuracy in predicting disease progression, with notable R² values and low MSE and RMSE values. The application of AI led to the successful identification of angiogenesis-related genes and biomarkers in various diseases, including diabetic foot ulcers and chronic obstructive pulmonary disease. AI-based image analysis also provided precise quantification of angiogenesis and inflammation, enhancing the understanding of disease mechanisms. Conclusion: AI-driven approaches significantly improve the analysis of complex biological processes, offering new insights into angiogenesis and inflammation. The high predictive accuracy of the AI models underscores their potential in clinical applications, such as personalized treatment strategies and disease monitoring. As AI continues to evolve, its integration into biomedical research will likely yield further advancements in disease prediction, diagnosis, and treatment.

The Use of Machine Learning in Predicting Health Trends

Machine learning (ML) is transforming the healthcare industry by enabling the analysis and prediction of health trends based on vast datasets. This paper investigates the application of machine learning in healthcare, focusing on the prediction of diseases, patient outcomes, and large-scale health trends such as epidemics and pandemics. Machine learning algorithms can process both structured and unstructured data from various sources, including wearable devices and social media, offering significant benefits in disease detection, patient care, and resource allocation. However, challenges such as data bias, privacy concerns, and model interpretability remain barriers to the widespread adoption of ML in predicting health trends. The paper discusses recent advancements, case studies, and future opportunities in this rapidly evolving field, illustrating the potential of machine learning to revolutionize health prediction while highlighting areas requiring further research and ethical considerations. Keywords: Machine learning, Health trend prediction, Healthcare data, Disease detection, Epidemics, Artificial intelligence.

Personalized Federated Graph Learning on Non-IID Electronic Health Records.

Understanding the latent disease patterns embedded in electronic health records (EHRs) is crucial for making precise and proactive healthcare decisions. Federated graph learning-based methods are commonly employed to extract complex disease patterns from the distributed EHRs without sharing the client-side raw data. However, the intrinsic characteristics of the distributed EHRs are typically non-independent and identically distributed (Non-IID), significantly bringing challenges related to data imbalance and leading to a notable decrease in the effectiveness of making healthcare decisions derived from the global model. To address these challenges, we introduce a novel personalized federated learning framework named PEARL, which is designed for disease prediction on Non-IID EHRs. Specifically, PEARL incorporates disease diagnostic code attention and admission record attention to extract patient embeddings from all EHRs. Then, PEARL integrates self-supervised learning into a federated learning framework to train a global model for hierarchical disease prediction. To improve the performance of the client model, we further introduce a fine-tuning scheme to personalize the global model using local EHRs. During the global model updating process, a differential privacy (DP) scheme is implemented, providing a high-level privacy guarantee. Extensive experiments conducted on the real-world MIMIC-III dataset validate the effectiveness of PEARL, demonstrating competitive results when compared with baselines.

Disease prediction with multi-omics and biomarkers empowers case–control genetic discoveries in the UK Biobank

The emergence of biobank-level datasets offers new opportunities to discover novel biomarkers and develop predictive algorithms for human disease. Here, we present an ensemble machine-learning framework (machine learning with phenotype associations, MILTON) utilizing a range of biomarkers to predict 3,213 diseases in the UK Biobank. Leveraging the UK Biobank’s longitudinal health record data, MILTON predicts incident disease cases undiagnosed at time of recruitment, largely outperforming available polygenic risk scores. We further demonstrate the utility of MILTON in augmenting genetic association analyses in a phenome-wide association study of 484,230 genome-sequenced samples, along with 46,327 samples with matched plasma proteomics data. This resulted in improved signals for 88 known (P < 1 × 10−8) gene–disease relationships alongside 182 gene–disease relationships that did not achieve genome-wide significance in the nonaugmented baseline cohorts. We validated these discoveries in the FinnGen biobank alongside two orthogonal machine-learning methods built for gene–disease prioritization. All extracted gene–disease associations and incident disease predictive biomarkers are publicly available (http://milton.public.cgr.astrazeneca.com).

Risk prediction of cardiovascular disease in the Asia-Pacific region: the SCORE2 Asia-Pacific model.

To improve upon the estimation of 10-year cardiovascular disease (CVD) event risk for individuals without prior CVD or diabetes mellitus in the Asia-Pacific region by systematic recalibration of the SCORE2 risk algorithm. The sex-specific and competing risk-adjusted SCORE2 algorithms were systematically recalibrated to reflect CVD incidence observed in four Asia-Pacific risk regions, defined according to country-level World Health Organization age- and sex-standardized CVD mortality rates. Using the same approach as applied for the original SCORE2 models, recalibration to each risk region was completed using expected CVD incidence and risk factor distributions from each region. Risk region-specific CVD incidence was estimated using CVD mortality and incidence data on 8,405,574 individuals (556,421 CVD events). For external validation, data from 9,560,266 individuals without previous CVD or diabetes were analysed in 13 prospective studies from 12 countries (350,550 incident CVD events). The pooled C-index of the SCORE2 Asia-Pacific algorithms in the external validation data sets was 0.710 (95% confidence interval [CI] 0.677-0.745). Cohort-specific C-indices ranged from 0.605 (95% CI 0.597-0.613) to 0.840 (95% CI 0.771-0.909). Estimated CVD risk varied several-fold across Asia-Pacific risk regions. For example, the estimated 10-year CVD risk for a 50-year-old non-smoker, with a systolic blood pressure of 140 mmHg, total cholesterol of 5.5 mmol/L, and high-density lipoprotein-cholesterol of 1.3 mmol/L, ranged from 7% for men in low-risk countries to 14% for men in very-high-risk countries, and from 3% for women in low-risk countries to 13% for women in very-high-risk countries. The SCORE2 Asia-Pacific algorithms have been calibrated to estimate 10-year risk of CVD for apparently healthy people in Asia and Oceania, thereby enhancing the identification of individuals at higher risk of developing CVD across the Asia-Pacific region.

Prediction of Multiple Degenerative Diseases Based on DNA Methylation in a Co-Physiology Mechanisms Perspective.

Degenerative diseases oftentimes occur within the continuous process of aging, and the corresponding clinical manifestations may be neurodegeneration, neoplastic diseases, or various human complex diseases. DNA methylation provides the opportunity to explore aging and degenerative diseases as epigenetic traits. It has already been applied to age prediction and disease diagnosis. It has been shown that various degenerative diseases share co-physiology mechanisms with each other, clues of which may be gained from studying the aging process. Here, we endeavor to predict the risk of degenerative diseases in an aging-relevant comorbid mechanism perspective. Firstly, an epigenetic clock method was implemented based on a multi-scale convolutional neural network, and a Shapley feature attribution analysis was applied to discover the aging-related CpG sites. Then, these sites were further screened to a smaller subset composed of 196 sites by using biomics analysis according to their biological functions and mechanisms. Finally, we constructed a multilayer perceptron (MLP)-based degenerative disease risk prediction model, Mlp-DDR, which was well trained and tested to accurately classify nine degenerative diseases. Recent studies also suggest that DNA methylation plays a significant role in conditions like osteoporosis and osteoarthritis, broadening the potential applications of our model. This approach significantly advances the ability to understand degenerative diseases and represents a substantial shift from traditional diagnostic methods. Despite the promising results, limitations regarding model complexity and dataset diversity suggest directions for future research, including the development of tissue-specific epigenetic clocks and the inclusion of a wider range of diseases.

A New Hybrid Model for Heart Disease Prediction Using Machine Learning Algorithms Optimized by Modified Whale Optimization Algorithm

Heart disease is a kind of cardiovascular disease (CVD) which globally considered death's number one cause. Data Science plays an important role in processing large data amounts in the healthcare domain. There are several problems that could hinder appropriate cardiac monitoring, including limited of medical dataset, lack of depth analysis, and feature selection. In this paper, we exploit the method of Fast Correlation-Based Feature Selection (FCBF) for filtering extra features for developing heart disease classification quality. Next, we implement the classification according to various algorithms of classification like Decision tree, Naïve Bayes, Logistic Regression, K-Nearest Neighbour (KNN), Random Forest, Support Vector Machine (SVM), and a Multilayer Perception which is optimized with the modified whale optimization algorithm evaluation version named Modified Whale Optimization Algorithm (MWOA). The proposed model optimized with FCBF as well as MWOA obtain an 83.26% accuracy score by Logistic Regression.

Machine Learning Approaches for Heart Disease Prediction: A Systematic Review and Comparative Analysis

Machine Learning Approaches for Heart Disease Prediction: A Systematic Review and Comparative Analysis

Optimizing the long short-term memory algorithm to improve the accuracy of infectious diseases prediction

&lt;p&gt;&lt;span lang="EN-US"&gt;This study discusses the implementation of the proposed optimized&lt;/span&gt;&lt;span lang="EN-US"&gt;long short-term memory (LSTM) to predict the number of infectious disease cases that spread in Central Java, Indonesia. The proposed model is developed by optimizing the output layer, which affects the output value of the cell state. This study used cases of four infectious diseases in Indonesia's Central Java Province, namely COVID-19, dengue, diarrhea, and hepatitis A. This model was compared to basic LSTM and MinMax schaler LSTM improvement to see the difference in the accuracy of each disease. The results showed a significant difference in the average prediction results with real cases between the three models. The main objectives of this study were: &lt;/span&gt;&lt;span lang="EN-US"&gt;modifying the LSTM algorithm to predict the number of infectious disease cases to get a smaller residual value&lt;/span&gt;&lt;span lang="EN-US"&gt;, &lt;span&gt;comparing the results of the optimization accuracy of the LSTM algorithm with the LSTM algorithm in previous studies, and evaluating the use of spatial variables in applying infectious disease prediction models using the LSTM algorithm. The results found that the performance difference between the proposed optimization algorithm and the model in the previous study was obtained. The proposed LSTM optimization algorithm had an accuracy improvement of about 2% over the previous model.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;