A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.

Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease management.With advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a challenge.This challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease pathogens.In this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data scarcity.This study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks.This paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate classification.The proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens detection.In future,more diseases can be included for detection along with some other modalities like pests and weed.

Health Systems Strengthening to Tackle the Global Burden of Pediatric and Congenital Heart Disease: A Diagonal Approach

1 Background Congenital heart disease(CHD)is the most common major congenital anomaly,affecting approximately one in every 100 live births[1].Among congenital anomalies,66%of preventable deaths are due to CHD,and 58%of the avertable morbidity and mortality due to congenital anomalies would result from scaling congenital heart surgery services[2].Every year,nearly 300,000 children and adults die from CHD,the majority of whom live in low-and middle-income countries(LMICs)[3].Approximately 49%of all individuals with CHD will require surgical or interventional care at some point in their lifetime[4];as a result of advances in access to and the delivery of such services,over 95%of children born with CHD in high-income countries now live into adulthood[3].Here,adults have surpassed children in the number of CHD cases at a ratio of 2:1[5].

Comparative Analysis of ARIMA and LSTM Model-Based Anomaly Detection for Unannotated Structural Health Monitoring Data in an Immersed Tunnel

Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.

Evaluation of dry eye disease symptomatology and mental health status among patients with different COVID-19 statuses

AIM:To evaluate dry eye disease(DED)symptomatology and mental health status in different COVID-19 patients.METHODS:A cross-sectional observational design was used.Totally 123 eligible adults(46.34%of men,age range,18-59y)with COVID-19 included in the study from August to November,2022.Ocular Surface Disease Index(OSDI),Five-item Dry Eye Questionnaire(DEQ-5),Hospital Anxiety and Depression Scale(HADS),and Pittsburgh Sleep Quality Index(PSQI)were used in this study.RESULTS:OSDI scores were 6.82(1.25,15.91)in asymptomatic carriers,7.35(2.50,18.38)in mild cases,and 16.67(4.43,28.04)in recurrent cases,with 30.00%,35.56%,and 57.89%,respectively evaluated as having DED symptoms(χ2=7.049,P=0.029).DEQ-5 score varied from 2.00(0,6.00)in asymptomatic carriers,3.00(0,8.00)in mild cases,and 8.00(5.00,10.00)in recurrent cases,with 27.50%,33.33%,and 55.26%,respectively assessed as having DED symptoms(χ2=8.532,P=0.014).The prevalence of clinical anxiety(50.00%)and depression(47.37%)symptoms were also significantly higher in patients with recurrent infection(χ2=24.541,P<0.001;χ2=30.871,P<0.001).Recurrent infection was a risk factor for high OSDI scores[odds ratio,2.562;95%confidence interval(CI),1.631-7.979;P=0.033]and DEQ-5 scores(odds ratio,3.353;95%CI,1.038-8.834;P=0.043),whereas having a fixed occupation was a protective factor for OSDI scores(odds ratio,0.088;95%CI,0.022-0.360;P=0.001)and DEQ-5 scores(odds ratio,0.126;95%CI,0.039-0.405;P=0.001).CONCLUSION:Patients with recurrent COVID-19 have more severe symptoms of DED,anxiety,and depression.

Impact of Social Determinants of Health on Self-Perceived Resilience: An Exploratory Study of Two Cohorts of Adults with Congenital Heart Disease

Social determinants of health(SDOH)affect quality of life.We investigated SDOH impacts on self-perceived resilience among people with adult congenital heart disease(ACHD).Secondary analysis of data from two com-plementary studies:a survey study conducted May 2021–June 2022 and a qualitative study conducted June 2020–August 2021.Resilience was assessed through CD-RISC10 score(range 0–40,higher scores reflect greater self-perceived resilience)and interview responses.Sociodemographic and SDOH(education,employment,living situa-tion,monetary stability,financial dependency,area deprivation index)data were collected by healthcare record review and self-report.We used linear regression with robust standard errors to analyze survey data and performed a thematic analysis of interview data.Survey participants(N=127)mean age was 42±14 years;51%were female,87%white.ACHD was moderate(75%)or complex(25%);41%functional class C or D.Resilience(mean 30±7)varied by monetary stability:compared to people with difficulty paying bills,resilience was 15.0 points higher(95%CI:6.9–23.1,p<0.001)for people reporting having enough money and 14.2 points higher(95%CI:5.9–22.4,p=0.001)for those reporting just enough money.Interview participants’(N=25)mean age was 32 years(range 22–44);52%were female,72%white.ACHD was moderate(56%)or complex(44%);76%functional class C or D.Participants discussed factors affecting resilience aligned with each of the major SDOH,prominently,economic stability and healthcare access and quality.Financial stability may be important for supporting self-perceived resi-lience in ACHD.This knowledge can inform the development of resilience interventions for this population.

Postpartum quality of life and mental health in women with heart disease:Integrated clinical communication and treatment

BACKGROUND Postpartum quality of life(QoL)in women with heart disease has been neglected.AIM To improve clinical communication and treatment,we integrated medical data and subjective characteristics to study postpartum QoL concerns.METHODS The study assessed QoL 6 wk after birth using the 12-Item Short-Form Health Survey.The Edinburgh Postnatal Depression Scale,Cardiac Anxiety Questionnaire,European Heart Failure Self-Care Behavior Scale,and a self-designed questionnaire based on earlier research were also used to assess patient characteristics.Patient data were collected.Prediction models were created using multiple linear regression.RESULTS This retrospective study examined postpartum QoL in 105 cardiac patients.Postpartum QoL scores were lower(90.69±13.82)than those of women without heart disease,with physical component scores(41.09±9.91)lower than mental component scores(49.60±14.87).Postpartum depression(33.3%),moderate anxiety(37.14%),pregnancy concerns(57.14%),offspring heart problems(57.14%),and life expectancy worries(48.6%)were all prevalent.No previous cardiac surgery,multiparity,higher sadness and cardiac anxiety,and fear of unfavorable pregnancy outcomes were strongly related to lower QoL(R^(2)=0.525).CONCLUSION Postpartum QoL is linked to physical and mental health in women with heart disease.Our study emphasizes the need for healthcare workers to recognize the unique characteristics of these women while developing and implementing comprehensive management approaches during their maternity care.

Observation on the Effect of Health Education in Health Management of Chronic Disease Patients

Objective: By the end of 2021, the aging rate of China’s population is 18.9%, and the prevalence rate of chronic diseases in the elderly population is increasing year by year, and chronic diseases have become the main causes of death and health threats of Chinese residents. Therefore, how to manage this huge group well is crucial. This paper analyzes the value of health education in the process of health management for patients with chronic diseases. Methods: 102 patients with chronic diseases treated from January 2021 to December 2021 were divided into control group and experimental group by random number table method. The control group was given routine health management while the experimental group was given health education based on the control group, and the implementation effect was analyzed. Results: After management, the scores of chronic disease knowledge in the experimental group were significantly higher than those in the control group, and the dimensions of ESCA were higher than those in the control group, and P < 0.05;Conclusion: The implementation of health education in the process of chronic disease health management is helpful to improve patients’ self-care ability and better control disease progression.

The Interventional Capacity of Community Health Volunteers for Screening and Linkages of Non-Communicable Diseases in Nyeri County, Kenya

Non-communicable diseases (NCDs) account for 63% of mortalities. Approximately 80% of these NCD-related deaths occur in LMICs. A quasi-experimental study utilizing a non-equivalent pre-and post-test was conducted from May 2022 to March 2023 with 370 study participants. Multistage cluster and random sampling were used to select ten community units, and therefore, 150 CHVs were chosen for the control unit, and 150 were used to form the interventional group. Data was collected from the KOBO app. Six (6) homogenous FGDs comprised ten members, and 10 KII were conducted across study sites. Quantitative data was analyzed using SPSS version 28.0, and qualitative data was audio-recorded, transcribed, and analyzed via N-Vivo 12. The study shows that 59.3% of respondents have minimal information, and 92.7% (n = 139) have no clear understanding of NCDs, with a pre-intervention capacity of 48.8%. Independent sample t-test showed a significant difference in capacity from a pre-intervention average of 48.75 (SD ± 5.7)%, which increased to 68.28 (SD ± 7.6)%, p < 0.001. A well-designed community interventional model plays a pivotal role in grassroots healthcare delivery but requires optimization for NCD management.

Effects of serum inflammatory factors,health index and disease activity scores on ankylosing spondylitis patients with sleep disorder

BACKGROUND Patients with ankylosing spondylitis(AS)frequently suffer from comorbid sleep disorders,exacerbating the burden of the disease and affecting their quality of life.AIM To investigate the clinical significance of serum inflammatory factors,health index and disease activity scores in patients with AS complicated by sleep disorders.METHODS A total of 106 AS patients with comorbid sleep disorders were included in the study.The patients were grouped into the desirable and undesirable prognosis groups in accordance with their clinical outcomes.The serum levels of inflammatory factors,including C-reactive protein,erythrocyte sedimentation rate,interleukin(IL)-6,tumour necrosis factor-αand IL-1β,were measured.Disease activity scores,such as the Bath AS functional index,Bath AS disease activity index,Bath AS metrology index and AS disease activity score,were assessed.The health index was obtained through the Short Form-36 questionnaire.RESULTS The study found significant associations amongst serum inflammatory factors,health index and disease activity scores in AS patients with comorbid sleep disorders.Positive correlations were found between serum inflammatory factors and disease activity scores,indicating the influence of heightened systemic inflammation on disease severity and functional impairment.Conversely,negative correlations were found between disease activity scores and health index parameters,highlighting the effect of disease activity on various aspects of healthrelated quality of life.Logistic regression analysis further confirmed the predictive value of these factors on patient outcomes,underscoring their potential utility in risk assessment and prognostication.CONCLUSION The findings demonstrate the intricate interplay amongst disease activity,systemic inflammation and patientreported health outcomes in AS patients complicated by sleep disorders.The results emphasise the need for comprehensive care strategies that address the diverse needs and challenges faced by these patients and underscore the potential relevance of serum inflammatory factors,health index and disease activity scores as prognostic markers in this patient population.

Effect of Health Management in Raising Awareness Rate of Disease Prevention and Treatment in Patients with Prehypertension

Objective: To analyze the effect of health management on improving the awareness rate of disease prevention and treatment in patients with prehypertension, so as to provide guidance for clinical management of patients with prehypertension. Methods: 108 patients diagnosed with prehypertension in our hospital were divided into a control group and an experimental group. The control group was not given management measures, while the experimental group was given health management. The incidence of hypertension and cognition level of hypertension knowledge were compared between the two groups after management. Results: The incidence of hypertension in the experimental group was 7.41% lower than that in the control group 29.63%. The cognitive level of hypertension in the patients (66.54 ± 1.25) was significantly higher than that in the patients without health management (41.45 ± 2.45), and P < 0.05;Conclusion: For patients with prehypertension, the implementation of health management is helpful to improve their cognition of hypertension, master related prevention knowledge, and reduce the incidence of hypertension.

Intestinal Microecology in Children with Pneumonia: The Relationship Between Digestive Health and Disease Recovery

This paper explores the association between intestinal microecology and digestive health and disease recovery in children with pneumonia.Intestinal microecological imbalance is common in children with pneumonia,which is closely associated with digestive health and disease recovery.Intestinal microecological imbalance may affect digestive enzyme activity,intestinal mucosal barrier function,and nutrient absorption,which in turn affects digestive health.In addition,intestinal microecological imbalances may be associated with immune regulation,inflammatory responses,and pathogen suppression,affecting disease recovery.Strategies to regulate intestinal microecology include probiotic supplementation,dietary modification,and pharmacological treatment.Currently,the study of intestinal microecology in children with pneumonia faces challenges,and there is a need for improved research methods,individualized treatment strategies,and the development of novel probiotics.In conclusion,the intestinal microecology of children with pneumonia is closely related to digestive health and disease recovery,and the regulation of intestinal microecology is of great significance to the treatment of children with pneumonia.Furthermore,future research should further explore the application of the microecology of the intestinal microecology in the treatment of children with pneumonia.

Application of Structured Health Education and Combined 5A Nursing Model in Self-control of Vulnerable Chronic Diseases: Take Coronary Heart Disease as an Example

Objective:To explore the intervention effect of the Structured Health Education course and 5A nursing model for self-control of elderly patients with coronary heart disease.Methods:Using the random sampling method,124 elderly CAD patients admitted to the First Affiliated Hospital of Bengbu Medical University were randomly divided into an experimental group and a control group.The control group line routine health education,experimental group take structured health education combined with 5A nursing before and after the intervention using a coronary heart disease assessment questionnaire,coronary heart disease self-control scale evaluation of two groups of intervention,compare two groups before and after intervention blood pressure,blood sugar,body mass index,lipid index level and complications within 8 months after discharge.Results:After the course intervention,the disease cognition and self-behavior of the experimental group were higher than that of the control group,and the differences were statistically significant(all P<0.1).Conclusion:This course is suitable for elderly patients with coronary heart disease.The 5A model improves the cognitive and management ability of elderly patients to a certain extent,which is worthy of clinical application.

Evaluation of the Application Effect of Enteral and Parenteral Nutrition Therapy Combined with a Health Belief Education Model in Patients with Inflammatory Bowel Disease

Objective:To evaluate the application effect of enteral and parenteral nutrition therapy combined with a health belief education model in patients with inflammatory bowel disease.Methods:80 patients with inflammatory bowel disease admitted to the Shanghai Zhangjiang Institute of Medical Innovation were chosen.This study was carried out from August 2022 to October 2023.The patients were randomly divided into a study group(40 cases)and a control group(40 cases).The treatment plan for the control group was the conventional treatment model,while the treatment plan for the study group was to provide enteral and parenteral nutrition therapy combined with a health belief education model based on the control group.The efficacy of both groups was compared.Results:In the study group,the therapeutic effect for 31 patients(77.50%)was markedly effective and 7 was effective(17.50%),accounting for 95.0%of the total,which was higher than the control group at 80.0%(P<0.05).The relief time of relevant symptoms in the study group was shorter than that of the control group(P<0.05).Before treatment,there were no differences in the high-sensitivity C-reactive protein(hs-CRP),interleukin 10(IL-10),and tumor necrosis factor-α(TNF-α)between both groups(P>0.05).After treatment,the levels of inflammatory factors in the study group(hs-CRP(8.02±1.13)mg/L,IL-10(9.24±1.25)pg/mL,and TNF-α(7.19±1.04)ng/L)were lower than those in the control group(P<0.05).Conclusion:Enteral and parenteral nutritional therapy combined with a health belief education model showed significant efficacy in inflammatory bowel disease patients.Patient symptoms were relieved and inflammatory reactions were reduced.This method is worthy of popularization.

Detection of Alzheimer’s disease onset using MRI and PET neuroimaging:longitudinal data analysis and machine learning

The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.

Towards Intelligent Detection and Classification of Rice Plant Diseases Based on Leaf Image Dataset

The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases.Deep Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing nodes.Further in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed method.Deep CNN is used for image recognition and is a class of deep-learning neural networks.CNN is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed model.After the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.

Relevance of Advanced Plant Disease Detection Techniques in Disease and Pest Management for Ensuring Food Security and Their Implication: A Review

Plant diseases and pests present significant challenges to global food security, leading to substantial losses in agricultural productivity and threatening environmental sustainability. As the world’s population grows, ensuring food availability becomes increasingly urgent. This review explores the significance of advanced plant disease detection techniques in disease and pest management for enhancing food security. Traditional plant disease detection methods often rely on visual inspection and are time-consuming and subjective. This leads to delayed interventions and ineffective control measures. However, recent advancements in remote sensing, imaging technologies, and molecular diagnostics offer powerful tools for early and precise disease detection. Big data analytics and machine learning play pivotal roles in analyzing vast and complex datasets, thus accurately identifying plant diseases and predicting disease occurrence and severity. We explore how prompt interventions employing advanced techniques enable more efficient disease control and concurrently minimize the environmental impact of conventional disease and pest management practices. Furthermore, we analyze and make future recommendations to improve the precision and sensitivity of current advanced detection techniques. We propose incorporating eco-evolutionary theories into research to enhance the understanding of pathogen spread in future climates and mitigate the risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with scientists, policymakers, and relevant intergovernmental organizations to ensure coordination and collaboration among them, ultimately developing effective disease monitoring and management strategies needed for securing sustainable food production and environmental well-being.

Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier

Alzheimer’s disease(AD)is a neurodevelopmental impairment that results in a person’s behavior,thinking,and memory loss.Themost common symptoms ofADare losingmemory and early aging.In addition to these,there are several serious impacts ofAD.However,the impact ofADcanbemitigatedby early-stagedetection though it cannot be cured permanently.Early-stage detection is the most challenging task for controlling and mitigating the impact of AD.The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the issue.To build a predictive model,open-source data was collected where five stages of images of AD were available as Cognitive Normal(CN),Early Mild Cognitive Impairment(EMCI),Mild Cognitive Impairment(MCI),Late Mild Cognitive Impairment(LMCI),and AD.Every stage of AD is considered as a class,and then the dataset was divided into three parts binary class,three class,and five class.In this research,we applied different preprocessing steps with augmentation techniques to efficiently identifyAD.It integrates a random oversampling technique to handle the imbalance problem from target classes,mitigating the model overfitting and biases.Then three machine learning classifiers,such as random forest(RF),K-Nearest neighbor(KNN),and support vector machine(SVM),and two deep learning methods,such as convolutional neuronal network(CNN)and artificial neural network(ANN)were applied on these datasets.After analyzing the performance of the used models and the datasets,it is found that CNN with binary class outperformed 88.20%accuracy.The result of the study indicates that the model is highly potential to detect AD in the initial phase.

Towards Sustainable Agricultural Systems:A Lightweight Deep Learning Model for Plant Disease Detection

A country’s economy heavily depends on agricultural development.However,due to several plant diseases,crop growth rate and quality are highly suffered.Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information.Therefore,the agricultural management system is searching for an automatic early disease detection technique.To this end,an efficient and lightweight Deep Learning(DL)-based framework(E-GreenNet)is proposed to overcome these problems and precisely classify the various diseases.In the end-to-end architecture,a MobileNetV3Smallmodel is utilized as a backbone that generates refined,discriminative,and prominent features.Moreover,the proposed model is trained over the PlantVillage(PV),Data Repository of Leaf Images(DRLI),and a new Plant Composite(PC)dataset individually,and later on test samples,its actual performance is evaluated.After extensive experimental analysis,the proposed model obtained 1.00%,0.96%and 0.99%accuracies on all three included datasets.Moreover,the proposed method achieves better inference speed when compared with other State-Of-The-Art(SOTA)approaches.In addition,a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pretrained models and other Machine Learning(ML)and DL methods.

Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System

Cardiovascular diseases are the most common cause of death worldwide over the last few decades in the developed as well as underdeveloped and developing countries. Early detection of cardiac diseases and continuous supervision of clinicians can reduce the mortality rate. However, accurate detection of heart diseases in all cases and consultation of a patient for 24 hours by a doctor is not available since it requires more sapience, time and expertise. In this?study, a tentative design of a cloud-based heart disease prediction system had been proposed to detect impending heart disease using Machine learning techniques. For the accurate detection of the heart disease, an efficient machine learning technique should be used which had been derived from a distinctive analysis among several machine learning algorithms in a Java Based Open Access Data Mining Platform, WEKA. The proposed algorithm was validated using two widely used open-access database, where 10-fold cross-validation is applied in order to analyze the performance of heart disease detection. An accuracy level of 97.53% accuracy was found from the SVM algorithm along with sensitivity and specificity of 97.50% and 94.94%respectively. Moreover, to monitor the heart disease patient round-the-clock by his/her caretaker/doctor, a real-time patient monitoring system was developed and presented using Arduino, capable of sensing some real-time parameters such as body temperature, blood pressure, humidity, heartbeat. The developed system can transmit the recorded data to a central server which are updated every 10 seconds. As a result, the doctors can visualize the patient’s real-time sensor data by using the application and start live video streaming if instant medication is required. Another important feature of the proposed system was that as soon as any real-time parameter of the patient exceeds the threshold, the prescribed doctor is notified at once through GSM technology.