Early Prediction Of Disease Research Articles

In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models Against Variability

Deep learning (DL)-based Human Activity Recognition (HAR) using wearable inertial measurement unit (IMU) sensors can revolutionize continuous health monitoring and early disease prediction. However, most DL HAR models are untested in their robustness to real-world variability, as they are trained on limited lab-controlled data. In this study, we isolated and analyzed the effects of the subject, device, position, and orientation variabilities on DL HAR models using the HARVAR and REALDISP datasets. The Maximum Mean Discrepancy (MMD) was used to quantify shifts in the data distribution caused by these variabilities, and the relationship between the distribution shifts and model performance was drawn. Our HARVAR results show that different types of variability significantly degraded the DL model performance, with an inverse relationship between the data distribution shifts and performance. The compounding effect of multiple variabilities studied using REALDISP further underscores the challenges of generalizing DL HAR models to real-world conditions. Analyzing these impacts highlights the need for more robust models that generalize effectively to real-world settings. The MMD proved valuable for explaining the performance drops, emphasizing its utility in evaluating distribution shifts in HAR data.

Early prediction of healthy ageing and age-related diseases using blood protein biomarkers.

Early prediction of healthy ageing and age-related diseases using blood protein biomarkers.

Liver function and Alzheimer's brain pathologies: A longitudinal study: Liver and Alzheimer's pathologies.

The neuropathological links underlying the association between changes in liver function and AD have not yet been clearly elucidated. We aimed to examine the relationship between liver function markers and longitudinal changes in Alzheimer's disease (AD) core pathologies. Data from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease, a longitudinal cohort study initiated in 2014, were utilized. Community and memory clinic setting. Three hundred forty-seven older adults. Participants underwent baseline and 2-year follow-up evaluations, including liver function assessments and various brain imaging techniques, such as amyloid and tau PET, FDG-PET, and MRI). Liver function indicators [alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total bilirubin] were examined as exposure variables. Higher baseline ALT levels were associated with a greater increase in beta-amyloid deposition over 2 years [β = 0.166, Bonferroni-corrected P (PB) = 0.012], while lower total bilirubin levels were associated with a greater increase in tau deposition over the same period (β = -0.570, PB < 0.001). In contrast, AST alone showed no significant association with changes of AD pathologies. The findings suggest a possible link between lower liver function and the accumulation of core AD pathologies in the brain. These results also support the possibility that the liver-brain axis could be a potential target for therapeutic or preventive strategies against AD.

Deep learning-based crop health enhancement through early disease prediction

Deep learning-based crop health enhancement through early disease prediction

Exploring Task-Related EEG for Cross-Subject Early Alzheimer's Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis.

The early prediction of Alzheimer's disease (AD) risk in healthy individuals remains a significant challenge. This study investigates the feasibility of task-state EEG signals for improving detection accuracy. Electroencephalogram (EEG) data were collected from the Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT). Time-frequency features were extracted using the Multitaper method, followed by multidimensional reduction techniques. Subspace features (F24 and F216) were selected via t-tests and False Discovery Rate (FDR) multiple comparisons correction, and subsequently analyzed in the Time-Frequency Area Average Test (TFAAT) and Prefrontal Beta Time Series Test (PBTST). The experimental results reveal that the MSIT task achieves optimal cross-subject classification performance using the Support Vector Machine (SVM) approach with the TFAAT feature set, yielding a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 58%. Similarly, the Sternberg Memory Task demonstrates classification ability with the logistic regression model applied to the PBTST feature set, emphasizing the beta band power spectrum in the prefrontal cortex as a potential marker of AD risk. These findings confirm that task-state EEG provides stronger classification potential compared to resting-state EEG, offering valuable insights for advancing early AD prediction research.

The clinical applications of dual-layer spectral detector CT in digestive system diseases.

Dual-layer spectral detector CT (DLCT) has several advantages in clinical practice, this study aims to reveal the clinical applications of DLCT in digestive system diseases. We searched PubMed and Cochrane Reviews for articles published from January 1, 2010 to May 31, 2024, using the terms "dual-layer spectral detector CT" or "dual-layer CT" combined with "hepatic fat" or "hepatic fibrosis" "hepatocellular carcinoma" or "pancreatic ductal adenocarcinoma" or "pancreatic neuroendocrine tumors" or "gastric cancer" or "colorectal cancer" or "Crohn's disease" or "bowel ischemia" or "acute abdominal conditions". DLCT consists of a top layer sensitive to lower-energy photons and a bottom layer sensitive to higher-energy photons. This configuration enables simultaneous acquisition of two energy spectra from a single X-ray beam ensuring consistent spatial alignment and temporal resolution. Spectral raw images allow image post-processing to improve image quality, reduce radiation doses and contrast media doses, and generate multiple quantitative parameters. It has broad potential for early detection, accurate staging, efficacy assessment, and prognosis prediction of liver, pancreatic, and gastrointestinal diseases, as well as for the assessment of digestive system vasculature. DLCT not only provides valuable information for the clinical diagnosis and therapeutic effect evaluation of digestive system diseases but also may play a more important role in the overall management of digestive diseases and in the decision-making of individualized medicine. Question What are the advantages of DLCT compared to traditional single-energy CT in the early detection, staging, and therapeutic evaluation of digestive system diseases? Findings DLCT enhances image quality, improves tissue characterization, and allows for multi-parametric analysis, making it superior in detecting and evaluating liver, pancreatic, and gastrointestinal diseases. Clinical relevance DLCT provides high-quality, multi-parametric imaging that improves the accuracy of diagnosing digestive diseases, facilitates more precise treatment planning, and enhances monitoring of treatment response, ultimately contributing to better patient management and prognosis.

Deep Learning Methods and UAV Technologies for Crop Disease Detection

The paper underscores the significant advancements in plant disease diagnostics achieved through the integration of remote sensing technologies and deep learning algorithms, particularly in aerial imagery interpretation. It focuses on evaluating deep learning techniques and unmanned aerial vehicles for crop disease detection. (Research purpose) The study aims to review and systemize scientific literature on the application of unmanned aerial vehicles, remote sensing technologies and deep learning 24 methods for the early detection and prediction of crop diseases. (Materials and methods) The paper presents various technologies employing unmanned aerial vehicles and sensors for monitoring plant condition, with an emphasis on modern computer vision tools designed to improve the accuracy of plant pathology identification. (Results and discussion) The analysis encompasses scientific publications from 2010 to 2023, with a primary focus on comparing the effectiveness of deep learning algorithms, such as convolutional neural networks (CNN), against traditional methods, including support vector machines (SVMs) and random forest classifiers. The findings demonstrate that deep learning algorithms offer more accurate and earlier detection of diseases, highlighting their potential for application in plant growing. The paper also addresses challenges associated with the use of unmanned aerial vehicles, such as data quality limitations, the complexity of processing large volumes of images, and the need for the development of more advanced models. The paper proposes solutions to these issues, including algorithm optimization and improved data preprocessing techniques. (Conclusions) The integration of unmanned aerial vehicles and deep learning provides new prospects for enhancing the efficiency of agricultural production. These technologies enable precise early-stage diagnosis of plant diseases and facilitate the prediction of their progression, allowing for timely implementation of crop protection measures. The combination of intelligent computer vision systems with unmanned aerial vehicles presents significant opportunities for advancing monitoring methods and improving plant health management.

Stretchable Multimodal Photonic Sensor for Wearable Multiparameter Health Monitoring.

Stretchable sensors that can conformally interface with the skins for wearable and real-time monitoring of skin deformations, temperature, and sweat biomarkers offer critical insights for early disease prediction and diagnosis. Integration of multiple modalities in a single stretchable sensor to simultaneously detect these stimuli would provide a more comprehensive understanding of human physiology, which, however, has yet to be achieved. Here, this work reports, for the first time, a stretchable multimodal photonic sensor capable of simultaneously detecting and discriminating strain deformations, temperature, and sweat pH. The multimodal sensing abilities are enabled by realization of multiple sensing mechanisms in a hydrogel-coated polydimethylsiloxane (PDMS) optical fiber (HPOF), featured with high flexibility, stretchability, and biocompatibility. The integrated mechanisms are designed to operate at distinct wavelengths to facilitate stimuli decoupling and employ a ratiometric detection strategy for improved robustness and accuracy. To simplify sensor interrogation, spectrally-resolved multiband emissions are generated upon the excitation of a single-wavelength laser, utilizing upconversion luminescence (UCL) and radiative energy transfer (RET) processes. As proof of concept, this work demonstrates the feasibility of simultaneous monitoring of the heartbeat, respiration, body temperature, and sweat pH of a person in real-time, with only a single sensor.

EPH177 A Multimodal Deep Neural Network for Early Prediction and Detection of Alzheimer's Disease Based on MRI Images and Clinical Information

EPH177 A Multimodal Deep Neural Network for Early Prediction and Detection of Alzheimer's Disease Based on MRI Images and Clinical Information

A Review on: Rice Disease Prediction

Abstract: Rice disease prediction is an emerging technology that leverages machine learning and image processing techniques to forecast disease outbreaks in rice crops. Early and accurate prediction of diseases like rice blast, bacterial leaf blight, and sheath blight is critical for maintaining crop health, optimizing resource use, and enhancing yield quality. This paper explores the use of high-resolution imaging and data-driven models to identify early signs of disease in rice plants. By analyzing data from multiple sources, including aerial imagery and environmental sensors, these predictive systems enable targeted and timely interventions, reducing the spread of infections and minimizing the use of pesticides. The application of predictive technologies not only aids in sustainable farming practices by lowering environmental impact but also aligns with the goals of precision agriculture to support food security and increase productivity. As agricultural practices continue to evolve, predictive models for rice disease management offer a promising solution for addressing the challenges posed by crop diseases in a changing climate

A framework for processing large-scale health data in medical higher-order correlation mining by quantum computing in smart healthcare.

This study aims to leverage the advanced capabilities of quantum computing to construct an efficient framework for processing large-scale health data, uncover potential higher-order correlations in medicine, and enhance the accuracy of smart healthcare diagnosis and treatment. A data processing framework is developed using quantum annealing algorithms and quantum circuits. We call it the quantum medical data simulation computational model (Q-MDSC). A unique encoding method based on quantum bits is employed for health data features, such as encoding symptom information from electronic health records into different quantum bits and representing different alleles of genetic data through superposition states of quantum bits. The properties of quantum entanglement are utilized to relate different data types, and quantum parallelism is harnessed to process multiple data combinations simultaneously. Additionally, this quantum computing framework is compared with traditional data mining methods using the same datasets, which include the Cochrane Systematic Review Database (https://www.cochranelibrary.com), the BioASQ Dataset (https://participants-area.bioasq.org), the PubMed Central Dataset (https://www.ncbi.nlm.nih.gov/pmc), and the Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov). The datasets are divided into training and testing sets in a 7:3 ratio during the experiments. Tests are conducted on association mining tasks of varying data scales and complexities, ranging from simple symptom-disease associations to complex gene-symptom-disease higher-order associations. The results indicate that, when processing large-scale data, the quantum computing framework improves overall computational speed by approximately 45% compared to traditional algorithms. Regarding uncovering higher-order correlations, the quantum computing framework enhances accuracy by about 30% relative to traditional algorithms. For early disease prediction, the accuracy achieved with the new framework is approximately 25% higher than that of conventional methods. Furthermore, for personalized treatment plan matching, the matching accuracy of the quantum computing framework surpasses traditional approaches by about 35%. These findings demonstrate the significant potential of the quantum computing-based smart healthcare framework for processing large-scale health data in the context of higher-order correlation mining, paving new pathways for the development of smart healthcare. This study utilizes multiple public datasets to achieve breakthroughs in computational speed, higher-order correlation mining, early disease prediction, and personalized treatment plan matching, thus opening new avenues for advancing smart healthcare.

Enhancing early Alzheimer's disease classification accuracy through the fusion of sMRI and rsMEG data: a deep learning approach.

Early detection and prediction of Alzheimer's Disease are paramount for elucidating neurodegenerative processes and enhancing cognitive resilience. Structural Magnetic Resonance Imaging (sMRI) provides insights into brain morphology, while resting-state Magnetoencephalography (rsMEG) elucidates functional aspects. However, inherent disparities between these multimodal neuroimaging modalities pose challenges to the effective integration of multimodal features. To address these challenges, we propose a deep learning-based multimodal classification framework for Alzheimer's disease, which harnesses the fusion of pivotal features from sMRI and rsMEG to augment classification precision. Utilizing the BioFIND dataset, classification trials were conducted on 163 Mild Cognitive Impairment cases and 144 cognitively Healthy Controls. The study findings demonstrate that the InterFusion method, combining sMRI and rsMEG data, achieved a classification accuracy of 0.827. This accuracy significantly surpassed the accuracies obtained by rsMEG only at 0.710 and sMRI only at 0.749. Moreover, the evaluation of different fusion techniques revealed that InterFusion outperformed both EarlyFusion with an accuracy of 0.756 and LateFusion with an accuracy of 0.801. Additionally, the study delved deeper into the role of different frequency band features of rsMEG in fusion by analyzing six frequency bands, thus expanding the diagnostic scope. These results highlight the value of integrating resting-state rsMEG and sMRI data in the early diagnosis of Alzheimer's disease, demonstrating significant potential in the field of neuroscience diagnostics.

Optimizing early diagnosis by integrating multiple classifiers for predicting brain stroke and critical diseases

Machine learning has gained attention in the medical field. Continuous efforts are being made to develop robust models for early prognosis purposes. The brain is the most pivotal organ in the human body. A brain stroke is generally caused by a blockage in the brain arteries. A brain stroke is one of the primary reasons for death. Therefore, early prediction of diseases like brain stroke, heart attack can significantly help in making decisions for doctors. The research study aims to find a robust and potential technique for the early prediction of brain stroke, Alzheimer’s, heart attack, cancer, Parkinson’s and potentially reducing the incidence of severe post complications of the mentioned diseases. By considering the five datasets as input, machine learning models have been trained for the research study. Early prediction of brain stroke has been done using eight individual classifiers along with 56 other models which are designed by merging the pairs of individual models using soft and hard voting for brain stroke and eight individual classifiers have been used for early prediction of heart attack, cancer, Alzheimer and Parkinson’s. After analyzing the results of each classifier for each disease, the proposed method, which is a pair of random forest and decision tree using a hard voting method for early brain stroke prediction, achieves the highest accuracy of 99%, which is better than all classifiers. Along with accuracy, the proposed method attained a value of 98% in precision, an outstanding 100% in recall, and 99% in F1 score. XGBoost performed best for cancer, Parkinson’s, Alzeihmer’s and Bernoulli naive bayes performed best in case of Heart attack .Upon comparing the values of these performance metrics, they outshine all the other model’s values.

Artificial Neural Networks with K-Fold Cross-Validation and Feature Selection for Early Heart Disease Prediction

Artificial Neural Networks with K-Fold Cross-Validation and Feature Selection for Early Heart Disease Prediction

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.

Genome-wide transcriptome analysis of Aβ deposition on PET in a Korean cohort.

Despite the recognized importance of including ethnic diversity in Alzheimer's disease (AD) research, substantial knowledge gaps remain, particularly in Asian populations. RNA sequencing was performed on blood samples from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease (KBASE) to perform differential gene expression (DGE), gene co-expression network, gene-set enrichment, and machine learning analyses for amyloid beta (Aβ) deposition on positron emission tomography. DGE analysis identified 265 dysregulated genes associated with Aβ deposition and replicated three AD-associated genes in an independent Korean cohort. Network analysis identified two modules related to pathways including a natural killer (NK) cell-mediated immunity. Machine learning analysis showed the classification of Aβ positivity improved with the inclusion of gene expression data. Our results in a Korean population suggest Aβ deposition-associated genes are enriched in NK cell-mediated immunity, providing a better understanding of AD molecular mechanisms and yielding potential diagnostic and therapeutic strategies. Dysregulated genes were associated with amyloid beta (Aβ) deposition on positron emission tomography in a Korean cohort. Dysregulated genes in Alzheimer's disease were replicated in an independent Korean cohort. Gene network moduleswere associated with Aβ deposition. Natural killer (NK) cell proportion in blood was associated with Aβdeposition. Dysregulated genes were related to a NK cell-mediated immunity.

Role and Impact of Method Noise on CT Image Denoising

Background The main emphasis of this study is on the medical Computed Tomography (CT) imaging denoising technique, which plays a major role in interpreting patient illness information for medical diagnosis. CT imaging is indispensable for accurate disease diagnosis, but image quality is affected by noise and other artifacts. The primary objective is to improve the accuracy of denoising algorithms, which consequently increases early disease prediction and enhances the accuracy of diagnostic outcomes. Objective The major objective was to examine the performance of method noise-based Low-dose CT (LDCT) image denoising technique using a Convolutional Neural Network (CNN) in medical imaging. This method aims to suppress noise, improve image quality, and effectively minimize radiation exposure. This method enhances the accuracy of the denoising algorithm, enabling more precise disease diagnosis. Method noise, or residual noise, plays a major role in denoising CT images while preserving fine details and minimizing other artifacts generated during the denoising process. Method noise includes the omitted structural features and other minute artifacts, which are resolved through CNN-based denoising techniques. This approach elevates the overall imaging quality and clarity, resulting in better diagnostic accuracy. Methods The study includes a systematic, method noise-based approach to determine the performance of denoising algorithms in diagnosing various diseases from medical CT images that are often affected by Gaussian noise. It involves selecting a comprehensive dataset, applying a method noise approach using CNN, and evaluating the outcomes through quantitative measures, such as PSNR, SNR, and SSIM. This comparison aims to assess diagnostic interpretation, thereby improving the accuracy and efficacy of the method noise-based technique in CT medical imaging. Results The results illustrate the differential accuracy and performance of CT image denoising techniques when compared to standard filtering methods, as well as after the application of method noise-based denoising techniques. Implementing quantitative measures, such as PSNR, SNR, and SSIM, aims to improve healthcare diagnostics. Conclusion The study concludes that method noise-based CT image denoising algorithms effectively mitigate noise and artifacts while retaining the corners, contours, and precise details of CT images, subsequently improving the efficiency and accuracy of predicting diagnostic results.

USING ARTIFICIAL INTELLIGENCE FOR BIOMARKER ANALYSIS IN CLINICAL DIAGNOSTICS

Introduction. Artificial intelligence (AI) technologies are becoming crucial in clinical diagnostics due to their ability to process and interpret large volumes of data. The implementation of AI for biomarker analysis opens new opportunities in personalized medicine, offering more accurate and individualized approaches to disease diagnosis and treatment. The relevance of this review stems from the need to systematize recent advances in AI application for biomarker analysis, which is critical for early diagnosis and prediction of chronic non-communicable diseases (NCDs). Material and methods. The analysis of peer-reviewed scientific publications and reports from leading research centers over the past five years was conducted. Studies on the application of AI algorithms for analyzing genomic, proteomic, and metabolomic biomarkers were reviewed, including machine learning methods and deep neural networks. Special attention was paid to the integration of multi-marker panels for improving the accuracy of diagnosis and prediction of cardiovascular, digestive, respiratory, endocrine system diseases, as well as oncological and neurodegenerative pathologies. Results. The application of AI has significantly increased the sensitivity and specificity of diagnostics, especially in complex cases requiring analysis of multiple disease parameters. The effectiveness of AI has been demonstrated in early diagnosis of lung, breast, and colorectal cancer, prediction of cardiovascular complications and NCDs progression, including diabetes mellitus and Alzheimer’s disease. AI’s significant contribution to the discovery of new biomarkers, optimization of personalized treatment, and improvement of therapeutic strategies has been noted. Conclusion. The use of AI in biomarker analysis has become a significant breakthrough in medical diagnostics, particularly in oncology, cardiology, and neurodegenerative diseases. The technology allows integration of data about various biomarkers and contributes to creating more accurate models for disease diagnosis and prediction. Further development is associated with technology advancement and overcoming ethical and regulatory barriers, which will expand AI capabilities in clinical practice.

Analysis of antinuclear antibody in 9 528 pregnant women during early pregnancy in a hospital in Qingdao City

To analyze the positivity rate and titer of antinuclear antibody (ANA), as well as nuclear pattern and target antigen of ANA in healthy pregnant women during early pregnancy in Qingdao area. A prospective cohort study design was used to include a total of 9 528 healthy pregnant women registered at the Women and Children's Hospital Affiliated to Qingdao University from March 2023 to June 2024.Indirect immunofluorescence assay (IIF) was used to detect ANA, its titer and cell staining pattern. Fifteen specific antibodies were tested using the magnetic bar code immunofluorescent luminescence method. Logistic regression model was used to analyze the risk factors of pregnancy with autoimmune disease(AID). The results showed that among 9 528 pregnant women in early pregnancy, 1 346 cases (14.1%) were positive of ANA, including 1 011 cases with a titer of 1∶100 (10.6%), 236 cases (2.5%) with a titer of 1∶320, and 99 cases (1.0%) were detected at a titer >1∶320. Among the 1 346 ANA-positive pregnant women, nuclear granular type accounted for the highest proportion (483 cases, 35.9%), followed by speckled type (347 cases, 25.8%) and cytoplasmic type (176 cases, 13.1%).Then, pregnant women with ANA titers ≥1∶100 were detected 15 specific antibodies.Anti-SSA was tested in 121 cases accounted for the majority, followed by 110 cases with anti-Ro-52, 56 cases with anti-SSB, 51 cases with anti-mitochondrial M2 subtype antibodies and 37 cases with anti-centromere B. In conclusion,in healthy pregnant women in Qingdao area, ANA positivity rate was 14.1%, and the titer of ANA was mainly at 1∶100.The predominant nuclear patterns were nuclear granular and speckled types.The specific autoantibodies were mainly anti-SSA antibodies and anti-Ro-52 antibodies.The detection of ANA and specific autoantibodies is of great significance for early prediction, diagnosis, and intervention of autoimmune diseases during pregnancy.

Plant Diseases Prediction Using Machine Learning

The early detection and accurate prediction of plant diseases are crucial for improving crop health and maximizing agricultural productivity. Traditional methods of disease detection, which rely heavily on manual observation, are often timeconsuming, labor-intensive, and prone to human error. Recent advancements in machine learning (ML) have opened new possibilities for developing efficient, automated systems that can predict plant diseases with high accuracy. This paper explores various machine learning techniques, including supervised and deep learning models, for plant disease prediction. By analyzing features such as leaf texture, color, and environmental data, these models can identify patterns indicative of specific diseases. Several datasets, including real-time image data and environmental metrics (such as humidity, temperature, and soil quality), are utilized to train and evaluate the models. The study focuses on key ML algorithms like Support Vector Machines (SVM), Decision Trees, Random Forests, Convolutional Neural Networks (CNN), and Transfer Learning to understand their efficacy in disease prediction. These models are evaluated based on their accuracy, precision, recall, and computational efficiency. The results demonstrate that deep learning models, particularly CNNs, outperform traditional methods in image-based plant disease identification. In addition, environmental data integration improves the predictive power of ML models, enabling proactive disease management strategies