Method For Early Detection Research Articles

Heart Disease Prediction Using Machine Learning

Heart disease remains a leading cause of mortality worldwide, necessitating improved methods for early detection and risk assessment. This paper reviews and analyzes the application of machine learning techniques in heart disease prediction, focusing on five primary algorithms: Naïve Bayes, k-Nearest Neighbor (KNN), Decision Tree, Artificial Neural Network (ANN), and Random Forest. By examining existing studies and datasets, we evaluate the effectiveness of these algorithms in predicting heart disease risk. Our analysis demonstrates that machine learning models can significantly enhance the accuracy and efficiency of heart disease prediction compared to traditional diagnostic methods. The Random Forest algorithm exhibited the highest overall performance, with studies reporting accuracy rates up to 95% in identifying potential heart disease cases. This review highlights the potential of machine learning in revolutionizing cardiovascular healthcare by enabling more personalized risk assessments and facilitating early intervention strategies. The integration of these advanced predictive models into clinical practice could substantially improve patient outcomes and reduce the global burden of heart disease. Keywords: Cardiovascular Risk Prediction, Machine Learning Algorithms, Electronic Health Records, Random Forest, Artificial Neural Networks, Feature Importance, Clinical Decision Support, Personalized Medicine, Predictive Analytics in Healthcare, Early Disease Detection.

Novel Efficient Selenium-Based D-π-A NIR Polymer Dots Anodic Electrochemiluminescence Emitter and Its Application in Simultaneous Detection of Two Pneumonia Pathogens with CdS Quantum Dots.

Community-acquired pneumonia (CAP) is a major cause of death in children under 5 years old globally. With Streptococcus pneumoniae (S. pneumoniae) and Mycoplasma pneumoniae (M. pneumoniae) being the main pathogens linked to CAP that requires hospitalization, there is an urgent need for a straightforward, cost-efficient, and highly accurate diagnostic method for immediate and early detection of CAP. In this work, benzo[1,2-c;4,5-c']bis([1,2,5]thiadiazole) (BBT) as π-bridge spacer with the donor unit of poly(9,9-dioctylfluorene) (PF) and the acceptor unit of dithienylbenzoselenadiazole (DBS) has been successfully copolymerized to unprecedentedly prepare novel D-π-A selenium-based polymer dots with efficient NIR electrochemiluminescence (named as Se-Pdots in this work). Se-Pdots exclusively generated excellent anodic ECL in the two-component coreaction system comprising TPrA and K2S2O8. Moreover, a potential-resolved ECL biosensor to simultaneously detect S. pneumoniae and M. pneumoniae has also been successfully constructed based on this novel Se-based NIR Pdots as an anodic emitter with CdS QDs as a cathodic emitter. Under optimal conditions, the biosensor has a wide linear range for S. pneumoniae (10-15 to 10-9 M) and M. pneumoniae (10-15 to 10-9 M), with low detection limits for S. pneumoniae (0.56 fM) and M. pneumoniae (0.96 fM). The proposed ECL biosensor provides a simple, sensitive, and reliable method for the simultaneous detection of CAP pathogens in clinical applications.

Current Understanding of the Role of Adenosine Receptors in Cancer.

Cancer, a complex array of diseases, involves the unbridled proliferation and dissemination of aberrant cells in the body, forming tumors that can infiltrate neighboring tissues and metastasize to distant sites. With over 200 types, each cancer has unique attributes, risks, and treatment avenues. Therapeutic options encompass surgery, chemotherapy, radiation therapy, hormone therapy, immunotherapy, targeted therapy, or a blend of these methods. Yet, these treatments face challenges like late-stage diagnoses, tumor diversity, severe side effects, drug resistance, targeted drug delivery hurdles, and cost barriers. Despite these hurdles, advancements in cancer research, encompassing biology, genetics, and treatment, have enhanced early detection methods, treatment options, and survival rates. Adenosine receptors (ARs), including A1, A2A, A2B, and A3 subtypes, exhibit diverse roles in cancer progression, sometimes promoting or inhibiting tumor growth depending on the receptor subtype, cancer type, and tumor microenvironment. Research on AR ligands has revealed promising anticancer effects in lab studies and animal models, hinting at their potential as cancer therapeutics. Understanding the intricate signaling pathways and interactions of adenosine receptors in cancer is pivotal for crafting targeted therapies that optimize benefits while mitigating drawbacks. This review delves into each adenosine receptor subtype's distinct roles and signaling pathways in cancer, shedding light on their potential as targets for improving cancer treatment outcomes.

DNA methylation urine test in the diagnosis of upper tract urothelial carcinoma: a systematic review and meta-analysis.

The DNA methylation urine test, a non-invasive early detection method for upper tract urothelial carcinoma (UTUC), is currently in full swing. This study aimed to systematically assess its diagnostic performance on UTUC. PubMed, Scopus, Embase, and Cochrane were our main databases when searching articles published from January 2000 to December 2023. Sensitivity and specificity were study primary endpoints. I2 was used to evaluated heterogeneity, meanwhile subgroup and meta-regression analyses were adopted to investigated the source of heterogeneity. Sensitivity analysis was performed to evaluate the result robustness, while Deeks' funnel plot asymmetry test was for the publication bias. Nine studies with 1326 patients were included. The pooled sensitivity was 0.89 (95% confidence interval (CI), 0.83-0.93) and specificity were 0.91 (95% CI, 0.83-0.96). The area under the receiver operating characteristic curve was 0.96 (95% CI, 0.93-0.97). Substantial heterogeneity was found during the data synthesis, whereas the pooled results remained robust in the sensitivity analysis. None of the potential covariates-urine sample collection method, population, country, methylation test method, or tumor grade-could account for the heterogeneity. DNA methylation urine test is a promising method with high efficiency for UTUC early detection. Nevertheless, owing to the significant heterogeneity, more well-organized studies are warranted to further explore its diagnostic efficiency and application context.

Soluble CD44 in oral rinses for the early detection of cancer: a prospective cohort study in high-risk individuals

BackgroundThere are 54,000 new cases of oral cavity and oropharyngeal cancer in the United States and more than 476,000 worldwide each year. Oral cavity and oropharyngeal squamous cell carcinoma make up most tumors with five-year survival rates of 50% due to prevalence of late-stage diagnoses. Improved methods of early detection in high-risk individuals are urgently needed. We aimed to assess the tumorigenic biomarkers soluble CD44 (solCD44) and total protein (TP) measured using oral rinses as affordable convenient screening tools for cancer detection.MethodsIn this prospective cohort study, we recruited 150 healthy current or former smokers through a community screening program. Baseline and four annual visits were conducted from March 2011-January 2016 with records followed until August 2020. Participants provided oral rinses, received head and neck exams, and completed questionnaires. SolCD44 and TP levels were measured and compared across groups and time. Participants were placed in the cancer group if malignancy developed in the study period, the suspicious group if physical exams were concerning for premalignant disease or cancer in the head and neck, and the healthy group if there were no suspicious findings. This analysis used two-sample t-test for comparison of means and two-sample Wilcoxon Test for comparison of medians. For subjects with follow-ups, estimated means of biomarkers were obtained from a fitted Repeated Measures Analysis of Variance (RANOVA) model including group, visit, and their interaction. Pairwise comparisons of mean solCD44 were made, including intergroup and intragroup comparison of values at different years.ResultsMost participants were males (58.7%), < 60 years of age. (90.7%), and Black (100%). Baseline mean solCD44 was elevated (2.781 ng/ml) in the cancer group compared to the suspicious group (1.849 ng/ml) and healthy group (1.779 ng/ml).ConclusionThis study supports the feasibility of a CD44-based oral rinse test as an affordable and convenient adjunctive tool for early detection of aerodigestive tract and other cancers in high-risk populations.

Reliability of impedance spectroscopy versus digital radiograph and ICDAS-II in occlusal caries detection: a prospective clinical trial

The traditional methods in early caries detection had many limitations. So, this study aimed to evaluate the clinical performance of alternating current impedance spectroscopy ACIST in comparison with digital radiograph and ICDAS-II in detection of occlusal carious lesions. Occlusal surfaces of molar and premolar teeth from 40 adult participants were examined by two observers using three diagnostic methods: (1) international caries detection and assessment system (ICDAS-II) (2) digital radiograph (DR) and (3) Cariescan Pro device (ACIST). Agreement analysis and the difference in sensitivities and specificities were evaluated. The results showed an excellent agreement in the different groups. The difference from the visual tactile against ACIST scoring for enamel caries detection, was statistically significant (p = 0.012) and the agreement was moderate (k = 0.509). For dentinal caries the difference was not statistically significant (p > 0.05) and the agreement was similarly moderate (k < 0.6). The difference from the digital radiograph against ACIST scoring, for enamel caries, digital radiography had significantly higher sensitivity and specificity than ACIST (p < 0.001) while for dentinal caries detection and overall, ACIST had higher sensitivity and digital radiography had higher specificity and the difference was statistically significant (p < 0.001). Visual-tactile examination is a considered as feasible and valid technique for occlusal caries detection, digital radiography is superior to ACIST in diagnosing enamel caries, but it could underestimate the caries depth, ACIST is a reliable tool for detecting occlusal caries in dentin.

A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction.

The fight against cancer, a relentless global health crisis, emphasizes the urgency for efficient and automated early detection methods. To address this critical need, this review assesses recent advances in non-invasive cancer prediction techniques, comparing conventional machine learning (CML) and deep neural networks (DNNs). Focusing on these seven major cancers, we analyze 310 publications spanning the years 2018 to 2024, focusing on detection accuracy as the key metric to identify the most effective predictive models, highlighting critical gaps in current methodologies, and suggesting directions for future research. We further delved into factors like datasets, features, and modalities to gain a comprehensive understanding of each approach's performance. Separate review tables for each cancer type and approach facilitated comparisons between top performers (accuracy exceeding 99%) and low performers (65.83 to 85.8%). Our exploration of public databases and commonly used classifiers revealed that optimal combinations of features, datasets, and models can achieve up to 100% accuracy for both CML and DNN. However, significant variations in accuracy (up to 35%) were observed, particularly when optimization was lacking. Notably, colorectal cancer exhibited the lowest accuracy (DNN 69%, CML 65.83%). A five-point comparative analysis (best/worst models, performance gap, average accuracy, and research trends) revealed that while DNN research is gaining momentum, CML approaches remain competitive, even outperforming DNN in some cases. This study presents an in-depth comparative analysis of CML and DNN techniques for cancer detection. This knowledge can inform future research directions and contribute to the development of increasingly accurate and reliable cancer detection tools.

Flood Monitoring Based on Remote Sensing of the Earth

The article deals with the problem of flooding of settlements due to floods, which causes economic damage and poses a threat to human life. Attention is paid to methods of early detection of water level rise to minimize the consequences. Modern remote sensing technologies, which complement traditional hydrological observation methods, present new opportunities for observation and forecasting of flood situations. Examples of remote sensing data and statistical data illustrate the effectiveness of modern technologies. The conclusion emphasizes the need for further improvement of flood detection technologies to enhance the effectiveness of flood risk management and ensure the safety of the population and economy of the regions.

Earliest Metabolic Changes Associated with the Initiation of Pancreatic Cancer.

Pancreatic cancer is usually detected at a late stage, when tumors have already metastasized; therefore, it has a poor prognosis with a 5-year survival rate of 11% to 12%. A key to targeting this high mortality is to develop methods for detecting the disease at a stage in which it is still local to the pancreas. However, this needs a better understanding of the events that govern pancreatic cancer oncogenesis. In this issue of Cancer Research, Neuß and colleagues report metabolic changes associated with acinar-to-ductal metaplasia (ADM), an initiating event that leads to the formation of precursor lesions for pancreatic ductal adenocarcinoma (PDAC). Their findings reveal a switch to aerobic glycolysis, increased c-MYC signaling, and increased serine metabolism as driving factors for the ADM process. These findings are important as they demonstrate that metabolic changes that drive the proliferation and metastasis of full-blown PDAC begin in the earliest lesions. The data not only provide insights into how PDAC develops but also a potential explanation for previously described findings, such as circulating lesion cells can be detected even when no carcinoma in situ is present. In summary, this article is highly relevant for furthering our understanding of how metabolic reprogramming drives the earliest events leading to PDAC development and could lay the groundwork for developing methods for early detection or intervention. See related article by Neuß et al., p. 2297.

A non-invasive screening method using Caenorhabditis elegans for early detection of multiple cancer types: A prospective clinical study

Cancer is the second leading cause of death worldwide, according to the World Health Organization, surpassed only by cardiovascular diseases. Early identification and intervention can significantly improve outcomes. However, finding a universal, non-invasive, economical, and precise method for early cancer detection remains a significant challenge. This study explores the efficacy of an innovative cancer detection test, N-NOSE, leveraging a Caenorhabditis elegans olfactory assay on urine samples across a diverse patient group exceeding 1600 individuals diagnosed with various cancers, with samples from the Shikoku Cancer Center (Ehime, Japan) under approved ethical standards. Current cancer screening techniques often require invasive procedures, can be painful or complex, with poor performance, and might be prohibitively costly, limiting accessibility for many. N-NOSE addresses these challenges head-on by offering a test based on urine analysis, eliminating the need for invasive methods, and being more affordable with higher performance at early stages than extensive blood tests or comprehensive body scans for cancer detection. In this study, N-NOSE demonstrated a capability to accurately identify upwards of 20 cancer types, achieving detection sensitivities between 60 and 90 %, including initial-stage cancers. The findings robustly advocate for N-NOSE's potential as a revolutionary, cost-effective, and minimally invasive strategy for broad-spectrum early cancer detection. It is also particularly significant in low- and middle-income countries with limited access to advanced cancer diagnostic methods, which may contribute to the improved outcome of affected individuals.

Exosomal miRNAs in prenatal diagnosis: Recent advances.

Exosomes, small membranous microvesicles released by cells, contain a range of bioactive molecules, including proteins and miRNAs, which play critical roles in intercellular communication and physiological and pathological processes. Current research suggests that exosomal miRNAs could serve as valuable biomarkers for prenatal diseases, offering a noninvasive method for early detection and monitoring. Studies linking exosomal miRNAs to various birth defects, including fetal growth restriction, urinary tract malformations, cardiovascular system malformations, and hereditary diseases like Down syndrome, were discussed. However, there are some conflicting study findings due to different exosome separation methods. Here, we also discussed exosome separation methods, emphasizing the importance of method selection based on specific purposes and sample types. Further studies are needed to standardize isolation techniques, understand the specific mechanisms underlying exosomal miRNA function, and develop reliable noninvasive prenatal diagnostic indicators. Overall, exosomal miRNAs show promise as potential biomarkers for prenatal diagnosis, but further research is necessary to validate their clinical utility.

Emotion expressions and cognitive impairments in theelderly: review of the contactless detection approach.

The aging population in Canada has been increasing continuously throughout the past decades. Amongst this demographic, around 11% suffer from some form of cognitive decline. While diagnosis through traditional means (i.e., Magnetic Resonance Imagings (MRIs), positron emission tomography (PET) scans, cognitive assessments, etc.) has been successful at detecting this decline, there remains unexplored measures of cognitive health that could reduce stress and cost for the elderly population, including approaches for early detection and preventive methods. Such efforts could additionally contribute to reducing the pressure and stress on the Canadian healthcare system, as well as improve the quality of life of the elderly population. Previous evidence has demonstrated emotional facial expressions being altered in individuals with various cognitive conditions such as dementias, mild cognitive impairment, and geriatric depression. This review highlights the commonalities among these cognitive health conditions, and research behind the contactless assessment methods to monitor the health and cognitive well-being of the elderly population through emotion expression. The contactless detection approach covered by this review includes automated facial expression analysis (AFEA), electroencephalogram (EEG) technologies and heart rate variability (HRV). In conclusion, a discussion of the potentials of the existing technologies and future direction of a novel assessment design through fusion of AFEA, EEG and HRV measures to increase detection of cognitive decline in a contactless and remote manner will be presented.

Exploring explainable AI features in the vocal biomarkers of lung disease

This review delves into the burgeoning field of explainable artificial intelligence (XAI) in the detection and analysis of lung diseases through vocal biomarkers. Lung diseases, often elusive in their early stages, pose a significant public health challenge. Recent advancements in AI have ushered in innovative methods for early detection, yet the black-box nature of many AI models limits their clinical applicability. XAI emerges as a pivotal tool, enhancing transparency and interpretability in AI-driven diagnostics. This review synthesizes current research on the application of XAI in analyzing vocal biomarkers for lung diseases, highlighting how these techniques elucidate the connections between specific vocal features and lung pathology. We critically examine the methodologies employed, the types of lung diseases studied, and the performance of various XAI models. The potential for XAI to aid in early detection, monitor disease progression, and personalize treatment strategies in pulmonary medicine is emphasized. Furthermore, this review identifies current challenges, including data heterogeneity and model generalizability, and proposes future directions for research. By offering a comprehensive analysis of explainable AI features in the context of lung disease detection, this review aims to bridge the gap between advanced computational approaches and clinical practice, paving the way for more transparent, reliable, and effective diagnostic tools.

Case Study on Analysing the Early Disease Detection of Pancreatic Ductal Adenocarcinoma in Korean Association for Clinical Oncology.

Pancreatic ductal adenocarcinoma (PDAC) is the most pervasive sort of pancreatic malignant growth. Due to the lack of early symptoms and effective methods for early detection and screening, the majority of patients (80% to 85%) are diagnosed with advanced metastatic or locally advanced disease, resulting in a low 5-year survival rate of 12%. The case study represents a comprehensive investigation into the intricate landscape of pancreatic cancer diagnosis within the Korean population. Grounded in epidemiological bits of knowledge, the review plans to disentangle the particular examples, commonness, and segment attributes of PDAC in Korea. By scrutinizing current diagnostic modalities, including conventional imaging techniques, molecular markers, and emerging technologies, the research seeks to evaluate the strengths and limitations of existing approaches within the Korean clinical context. Central to the study is an exploration of the collaborative initiatives spearheaded by the Association of Clinical Oncology in Korea in the domain of PDAC early detection. Analysing research projects, clinical trials, and interdisciplinary collaborations, the case study sheds light on the association's pivotal role in driving innovation and progress in oncology. The goal is to offer a detailed analysis of how the association helps in furthering knowledge and enhancing results in the management of PDAC. The case study delves into the implications of early PDAC detection for patient outcomes, emphasizing the significance of timely interventions and tailored treatment strategies. By outlining the potential benefits and challenges associated with early diagnosis, the study aims to inform health care policies, shape clinical guidelines, and guide future research priorities. Through a holistic approach, the case study endeavours to offer important experiences into the multifaceted landscape of PDAC early detection within the Korean health care system, contributing to the broader discourse on effective oncological practices and patient care.

Real-time multivariate statistical monitoring of biopharmaceutical processes with no prior product-specific history

Biologics drug substance manufacturing normally comprises several processing steps including cell culture, purification, etc. Real-time Multivariate Statistical Process Monitoring (RT-MSPM) is an integral need in order to achieve early fault detection and prevention to guarantee robust and efficient process performance. As a part of RT-MSPM framework, Batch Evolution Models (BEMs), which take into account the transient phase of a batch prior to reaching full maturity (steady state), are the standard methods for real-time early fault detection in batch processes. Prior product-specific knowledge and training dataset are critical in order to model time (batch maturity) dependencies. Unlike traditional settings, modern biopharmaceutical manufacturing and clinical facilities use modular designs to enable simultaneous production of multiple medicines and quickly pivot from producing one medicine to another where no prior product-specific historical data might be available. In this article, first, a framework is proposed to detect the steady state operation of a manufacturing scale cell culture bioreactor. This is critical to achieve the ultimate goal to demonstrate feasibility of developing a continuous statistical process monitoring framework to monitor the steady state operation of a batch with no prior product specific history. Steady state time series data from previous step are processed following a systematic approach. The requirements for statistical properties of the processed time series in order to leverage data from different products to monitor a new product are outlined. The new framework establishes a guideline to develop models using datasets from other products with different recipes to monitor new products with no prior product-specific history. A novel software product is developed to test the framework. The framework is successfully applied for real-time monitoring of fast-rate sensors of a manufacturing cell culture bioreactor.

Screening for psychosis risk in primary mental health care services - Implementation, prevalence and recovery trajectories.

Early interventions improve outcomes for people at high risk of psychosis and are likely to be cost saving. This group tends to seek help for emotional problems - depression and anxiety - via primary care services, where early detection methods are poor. We sought to determine prevalence rates of high risk for psychosis in UK primary care mental health services and clinical outcomes following routinely delivered psychological therapies. We used a brief screen designed for settings with low base rates and significant time constraints to determine prevalence of high risk for psychosis in UK 'Talking Therapies' services. We examined socio-demographic characteristics, presenting problems and recovery trajectories for this group, compared with people not at risk of psychosis. A 2-item screen selected for specificity yielded a prevalence rate of 3% in primary care mental health services. People at elevated risk of psychosis were younger and more likely to report at least one long-term physical condition. This group presented with higher levels of depression, anxiety and trauma symptoms at assessment and were less likely to have recovered at the end of treatment, compared to people not at risk. Very brief screening tools can be implemented in busy health care settings. The 3% of referrals to UK primary care psychological therapies services at elevated risk of psychosis typically present with more severe symptoms and greater levels of comorbidity and may require augmented interventions to recover fully.

Potential diagnostic, prognostic, and predictive biomarkers of gastric cancer.

Gastric cancer (GC), a malignant epithelial tumor, is the fourth leading cause of cancer-related death worldwide. Therapeutic strategies for GC, despite the biggest challenges, can significantly improve survival rates through early detection and effective screening methods. To provide brief information on the necessity of multiple specific diagnostic, prognostic, and predictive markers for GC. This review was conducted using a variety of search engines, including PubMed Central, Scopus, Web of Science, Google Scholar, and others. Some potential biomarkers that provide essential information include circulating tumor cells (CTCs), DNA methylation, claudin 18.2, fibroblast growth factor receptor 2 (FGFR2), long noncoding RNAs (lncRNAs), cell-free DNA (cfDNA), microRNAs, and serum pepsinogens. Multiple tumor markers are essential for screening, tumor identification, staging, prognostic assessment, and monitoring recurrence after therapy due to the absence of a single tumor indicator for diagnosing, prognosticating, and predicting GC.

Oral mucosal lesions triage via YOLOv7 models

Background/purposeThe global incidence of lip and oral cavity cancer continues to rise, necessitating improved early detection methods. This study leverages the capabilities of computer vision and deep learning to enhance the early detection and classification of oral mucosal lesions. MethodsA dataset initially consisting of 6903 white-light macroscopic images collected from 2006 to 2013 was expanded to over 50,000 images to train the YOLOv7 deep learning model. Lesions were categorized into three referral grades: benign (green), potentially malignant (yellow), and malignant (red), facilitating efficient triage. ResultsThe YOLOv7 models, particularly the YOLOv7-E6, demonstrated high precision and recall across all lesion categories. The YOLOv7-D6 model excelled at identifying malignant lesions with notable precision, recall, and F1 scores. Enhancements, including the integration of coordinate attention in the YOLOv7-D6-CA model, significantly improved the accuracy of lesion classification. ConclusionThe study underscores the robust comparison of various YOLOv7 model configurations in the classification to triage oral lesions. The overall results highlight the potential of deep learning models to contribute to the early detection of oral cancers, offering valuable tools for both clinical settings and remote screening applications.

LUNG CANCER SCREENING WITH LOW-DOSE COMPUTED TOMOGRAPHY: A LITERATURE REVIEW

Relevance: Lung cancer (LC) is the leading cause of death among oncological diseases and occupies a leading position in the number of new cases worldwide. Early detection of symptoms, precise diagnosis, and effective treatment are crucial for increasing lung cancer survival rates. Low-dose computed tomography (LDCT) screening programs represent a promising method for early lung cancer detection, which can substantially decrease mortality rates and improve patient survival prospects. The study aimed to review foreign scientific publications on lung cancer screening using low-dose computed tomography. Methods: The study included the results of foreign randomized controlled trials (RCTs) of lung cancer screening using LDCT, based on articles, meta-analyses, systematic reviews, and reviews. Exclusion criteria included publications published before 2013; publications that were not publicly available. The search results yielded 369 articles, of which 39 literary sources were included in this review. The search covered a depth of 10 years (2013-2023). Results: An analysis of foreign studies was performed that confirmed the effectiveness of low-dose computed tomography in the detection of lung cancer. Data from randomized controlled trials, meta-analyses, systematic reviews and reviews are presented, confirming the potential of this method to improve early stage lung cancer detection, thus improving survival rates and reducing mortality. Conclusion: LDCT has been shown to be effective in reducing lung cancer mortality, as confirmed by the results of numerous studies. However, the strategy to implement the lung cancer screening program should be specific to each country in which it will be implemented, taking into account the specifics of the health system. To achieve optimal results, it is necessary to continue research related to the implementation of this screening method, including an analysis of its economic feasibility, as well as an assessment of long-term results and potential undesirable effects.

Application of artificial neural network method for early detection of dengue fever

Application of artificial neural network method for early detection of dengue fever