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Machine Learning Techniques for Automated Nuclear Atypia Detection in Histopathology Images: A Review
Nuclear atypia identification is an important stage in pathology procedures for breast cancer diagnosis and prognosis. The introduction of image processing techniques to automate nuclear atypia identification has made the very tedious, error-prone, and time-consuming procedure of manually observing stained histopathological slides much easier. In the last decade, several solutions for resolving this problem have emerged in the literature, and they have shown positive incremental advancements in this fieldof study. The nuclear atypia count is an important measure to consider when assessing breast cancer. This work provides a comprehensive review of automated nuclear atypia scoring process which includes the current advancements and future prospects for this critical undertaking, which will aid humanity in the fight against cancer. In this study, we examine the various techniques applied in detecting nuclear atypiain breast cancer as well as the major hurdles that must be overcome and the use of benchmark datasets in this domain. This work provides a comprehensive review of automated nuclear atypia scoring process which includes the current advancements and prospects for this critical undertaking, which will aid humanity in the fight against cancer. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Machine Learning Techniques for Resource-Constrained Devices in IoT Applications with CP-ABE Scheme
Ciphertext-policy attribute-based encryption (CP-ABE) is one of the promising schemes which provides security and fine-grain access control for outsourced data. The emergence of cloud computing allows many organizations to store their data, even sensitive data, in cloud storage. This raises the concern of security and access control of stored data in a third-party service provider. To solve this problem, CP-ABE can be used. CP-ABE cannot only be used in cloud computing but can also be used in other areas such as machine learning (ML) and the Internet of things (IoT). In this paper, the main focus is discussing the use of the CP-ABE scheme in different areas mainly ML and IoT. In ML, data sets are trained, and they can be used for decision-making in the CP-ABE scheme in several scenarios. IoT devices are mostly resource-constrained and has to process huge amounts of data so these kinds of resource-constrained devices cannot use the CP-ABE scheme. So, some solutions for these problems are discussed in this paper. Two security schemes used in resource-constrained devices are discussed. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Machine Learning Techniques in Predicting Heart Disease a Survey
The heart serves an important role in living creatures. Diagnosis and forecast of cardiac illnesses demand greater precision, perfection, and accuracy because such tiny mistakes can lead to weariness and death. Numerous heart-related deaths have occurred, and the incidence rates have been rising over time. Predicting the development of heart disorders is important to work in the medical industry. Every month, many databases related to the patient are kept. The information gathered can be used to predict the occurrence of future diseases. This article gives an outline of cardiovascular diseases and modern treatments. Also, the focus of this research is to outline some current research on applying machine learning techniques to predict heart disease, analyze the many machine learning algorithms employed, and determine which technique(s) are useful and efficient. Artificial neural network (ANN), decision tree (DT), fuzzy logic, K-nearest neighbor (KNN), Naive bayes (NB), and support vector machine (SVM) are data mining and machine learning approaches used to predict cardiac disease. This paper includes an overview of the present method based on features, the algorithms are compared, and the most accurate algorithm is analyzed. 2022 IEEE. -
Machine Learning Technology-Based Heart Disease Detection Models
At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Nae Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared. Copyright 2022 Umarani Nagavelli et al. -
Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images
In recent times, the healthcare industry has been generating a significant amount of data in distinct formats, such as electronic health records (EHR), clinical trials, genetic data, payments, scientific articles, wearables, and care management databases. Data science is useful for analysis (pattern recognition, hypothesis testing, risk valuation) and prediction. The major, primary usage of data science in the healthcare domain is in medical imaging. At the same time, lung cancer diagnosis has become a hot research topic, as automated disease detection poses numerous benefits. Although numerous approaches have existed in the literature for lung cancer diagnosis, the design of a novel model to automatically identify lung cancer is a challenging task. In this view, this paper designs an automated machine learning (ML) with data science-enabled lung cancer diagnosis and classification (MLDS-LCDC) using computed tomography (CT) images. The presented model initially employs Gaussian filtering (GF)-based pre-processing technique on the CT images collected from the lung cancer database. Besides, they are fed into the normalized cuts (Ncuts) technique where the nodule in the pre-processed image can be determined. Moreover, the oriented FAST and rotated BRIEF (ORB) technique is applied as a feature extractor. At last, sunflower optimization-based wavelet neural network (SFO-WNN) model is employed for the classification of lung cancer. In order to examine the diagnostic outcome of the MLDS-LCDC model, a set of experiments were carried out and the results are investigated in terms of different aspects. The resultant values demonstrated the effectiveness of the MLDS-LCDC model over the other state-of-The-Art methods with the maximum sensitivity of 97.01%, specificity of 98.64%, and accuracy of 98.11%. 2023 World Scientific Publishing Company. -
Machine Learning-Based Classical Dance Mudra Recognition Model
In this research, symbolic hand mudras of the Indian traditional dance style of Bharatanatyam are recognized and categorized using deep learning techniques. The three main goals are establishing baseline datasets to identify and categorize hasta mudras, designing an automated tutoring program for prospective students, and constructing a system for recommending videos that support cultural heritage. The research achieves a real-time recognition accuracy of 85% to 95% using convolutional neural networks (CNNs) and the Mobile Net architecture. This activity greatly aids virtual learning during pandemics, worldwide cultural relations, and preserving intangible cultural assets. The three main goals of this research are to establish baseline datasets for accurate mudra identification, create an automated tutoring program for participants, and build a video recommendation system to promote cultural heritage globally. The benchmark datasets that are used to train the models are made up of high-quality photos and videos of mudras that are taken and annotated under the direction of experts. While the video recommendation system supports attempts to preserve culture and advance education, the automated tutoring system provides participants with a comprehensive virtual learning environment and tailored feedback. To ensure the survival and continued appreciation of Bharatanatyam around the world, our endeavor substantially enhances virtual education, deep learning, and cultural preservation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Machine Learning-Based Classification of Autism Spectrum Disorder across Age Groups
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that has gained significant attention in recent years due to its increasing prevalence and profound impact on individuals, families, and society as a whole. In this study, we explore the use of different machine learning classifiers for the accurate detection of ASD in children, adolescents, and adults. Furthermore, we conduct feature reduction to identify key features contributing to ASD classification within each age group using Cuckoo Search Algorithm. Logistic Regression has the highest accuracy compared to the other two models. 2024 by the authors. -
Machine Learning-Based Credit Scoring for Personalized and Inclusive Lending in Consumer-Centric Financial Systems
Traditional approaches to credit-scoring are largely based on rule systems that can be excessively fixed and limited to the ability to reflect individual financial behavior. The article analyzes the effectiveness of machine-learning (ML)- based credit ranking with the hypothesis that they can improve predictive capability and fairness of consumer credit lending. The performance of these algorithms, including supervised methods of learning, e.g., logistic regression, random forests as well as the deep learning, is contrasted to the conventional credit models. Model transparency is provided by SHAP values and other methods explainable by AI. Findings show that practice based on the use of ML outperform traditional methods in risk assessment, especially, through the inclusion of supplementary forms of data in traditional databases based on transaction behavior, virtual footprints, and psychometric signals. Furthermore, ethical standards and moral confidence in ML informed credit decision-making will require regulation-proof and explanatory modelling. Through the research, it is recommended to implement policy measures intended to cause financial institutions, fintech companies, and regulating bodies to implement ML-based credit-scoring technologies, with fairness and predictive effectiveness being reciprocal drivers of financial access and consumer-friendly lending practices. 2025 IEEE. -
Machine Learning-based Currency Information Retrieval for Aiding the Visually Impaired People
Paper currency is one of the most in-demand and long-established payment modes across the globe. People suffering from visual disabilities often face difficulties while handling paper currencies. Over the years, assisting technology has been rekindling itself to serve the aged and disabled person more aptly. Image processing methods and other sophisticated technologies, like Artificial Intelligence, Deep Learning, etc., can be employed to identify banknotes and fetch other valuable pieces of information from them. This paper proposes a framework that focuses on an integrated approach to retrieving data from the paper currency's uploaded image. The current version of the framework focuses on identifying the authenticity of the paper currency and classifying it according to its value. This work is an initiative to help visually impaired people to use paper currencies without assistance from other individuals and support them in living independently. 2021 IEEE. -
Machine Learning-Based Driver Assistance System Ensuring Road Safety for Smart Cities
Technologies around smart city and green computing are gaining more and more interest from diversified workforce areas. The transportation system is one of them. The transportation vehicles are operating day and night to provide proper support for the need. This is really tiring for the transportation workers, especially the drivers who are driving the vehicle. A slight negligence of a driver may cause a huge loss. The increasing number of road accidents is therefore a big concern. Research works are going on to comfort the drivers and increase the security features of vehicle to avoid accidents. In this chapter, a model is proposed, which can efficiently detect drivers drowsiness. The discussion mainly focuses on building the learning model. A modified convolution neural network is built to solve the purpose. The model is trained with a dataset of 7000 images of open and closed eyes. For testing purpose, some real-time experiments are done by some volunteer drivers in different conditions, like gender, day, and night. The model is really good for daytime and if the driver is not wearing any glass. But with a glass in the eyes and in night condition, the system needs improvements. 2025 selection and editorial matter, Yousef Farhaoui, Bharat Bhushan, Nidhi Sindhwani, Rohit Anand, Agbotiname Lucky Imoize and Anshul Verma; individual chapters, the contributors. -
Machine Learning-Based Imputation Techniques Analysis and Study
Missing values are a significant problem in data analysis and machine learning applications. This study looks at the efficacy of machine learning (ML) - based imputation strategies for dealing with missing data. K-nearest Neighbours (KNN), Random Forest, Support Vector Machines (SVM), and Median/Mean Imputation were among the techniques explored. To address the issue of missing data, the study employs k-nearest neighbors, Random Forests, and SVM algorithms. The dataset's imbalance is considered, and the mean F1 score is employed as an evaluation criterion, using cross-validation to ensure consistent results. The study aims to identify the most effective imputation strategy within ML models, offering crucial insights about their adaptability across various scenarios. The study aims to determine the best plan for data preprocessing in machine learning by comparing approaches. Finally, the findings help to improve our knowledge and application of imputation techniques in real-world data analysis and machine learning. 2024 IEEE. -
Machine Learning-Based Intrusion Detection Systems for 5G and beyond Networks
NextGen networks (5 G and beyond) have diversified their infrastructure. Traditional Intrusion Detection Systems (IDS) cannot effectively address the continuously evolving landscape of threats, which is why machine learning-based IDS has emerged as a crucial solution. This overview presents the trends in the application of machine learning techniques (deep learning and ensemble methods) for machine learning-based intrusion detection in 5 G and beyond networks. The important issues tackled encompass real-time anomaly detection, large-scale data processing, adaptive learning against unknown attacks, and detection outcomes. Specifically, we emphasize the promising combination of federated learning, reinforcement learning, and graph-based methods for deployment in distributed, resource-constrained network environments. We present a comprehensive overview of performance metrics such as accuracy, false positive rate, computational overhead, and scalability for each approach, highlighting the crucial trade-offs necessary for successful deployment in dynamic 5G scenarios. Furthermore, we prioritize privacy-preserving methods and secure model sharing. This abstract could further highlight that machine learning-based schemes for intrusion detection systems are important additions toward providing strong defences for cyberspace in 5 G and beyond. 2025 IEEE. -
Machine Learning-Based Maternal and Child Mortality Rate Prediction Using Random Forest Algorithm
This research uses a variety of data sources such as maternal age, health records of the mother and/or child, socioeconomic status, medical history, or prenatal care, and details of health indicators to determine the factors most decisive in increasing mortality risks. This entails data acquisition, data cleaning, data transformation and selection, and model building with an example of algorithms such as logistic regression and random forest. The trained models are checked for accuracy and their resilience level is checked using methods like SHapley Additive exPlanations and Local Interpretable Model agnostic Explanations for interpretation. The model is presented in an easy interface that doctors and health practitioners could use to make early and relevant decisions. It keeps updating the performance of established models and is a crucial way of maintaining data security for compliance with the set regulations. The rationale for this project is to offer practical recommendations for healthcare technicians so that more lives of mothers and children could be saved and maternal/child mortality decreased. Random Forest, in particular, has demonstrated superiority due to its ensemble approach, which mixes many decision trees to improve forecast accuracy and robustness. This technique can handle huge datasets with increased dimensionality and effectively lowers the overfitting risk. Additionally, Random Forest improves generalization by averaging the outputs of numerous trees, making it more tolerant to data noise and fluctuation. What makes it superior to single decision tree models is that it can handle both numerical and categorical data and handle missing values without a substantial loss of accuracy. 2025 selection and editorial matter, Babita Singla, Kumar Shalender, Nripendra Singh, and Sandhir Sharma; individual chapters, the contributors. -
Machine Learning-Driven Energy Management for Electric Vehicles in Renewable Microgrids
The surge in demand for sustainable transportation has accelerated the adoption of electric vehicles (EVs). Despite their benefits, EVs face challenges such as limited driving range and frequent recharging needs. Addressing these issues, innovative energy optimization techniques have emerged, prominently featuring machine learning-driven solutions. This paper reviews work in the areas of Smart EV energy optimization systems that leverage machine learning to analyse historical driving data. By understanding driving patterns, road conditions, weather, and traffic, these systems can predict and optimize EV energy consumption, thereby minimizing waste and extending driving range. Concurrently, renewable microgrids present a promising avenue for bolstering power system security, reliability, and operation. Incorporating diverse renewable sources, these microgrids play a pivotal role in curbing greenhouse gas emissions and enhancing efficiency. The review also delves into machine learning-based energy management in renewable microgrids with a focus on reconfigurable structures. Advanced techniques, such as support vector machines, are employed to model and estimate the charging demand of hybrid electric vehicles (HEVs). Through strategic charging scenarios and innovative optimization methods, these approaches demonstrate significant improvements in microgrid operation costs and charging demand prediction accuracy. The Authors, published by EDP Sciences, 2024. -
Machine Learning-Enabled NIR Spectroscopy. Part 3: Hyperparameter by Design (HyD) Based ANN-MLP Optimization, Model Generalizability, and Model Transferability
Data variations, library changes, and poorly tuned hyperparameters can cause failures in data-driven modelling. In such scenarios, model drift, a gradual shift in model performance, can lead to inaccurate predictions. Monitoring and mitigating drift are vital to maintain model effectiveness. USFDA and ICH regulate pharmaceutical variation with scientific risk-based approaches. In this study, the hyperparameter optimization for the Artificial Neural Network Multilayer Perceptron (ANN-MLP) was investigated using open-source data. The design of experiments (DoE) approach in combination with target drift prediction and statistical process control (SPC) was employed to achieve this objective. First, pre-screening and optimization DoEs were conducted on lab-scale data, serving as internal validation data, to identify the design space and control space. The regression performance metrics were carefully monitored to ensure the right set of hyperparameters was selected, optimizing the modelling time and storage requirements. Before extending the analysis to external validation data, a drift analysis on the target variable was performed. This aimed to determine if the external data fell within the studied range or required retraining of the model. Although a drift was observed, the external data remained well within the range of the internal validation data. Subsequently, trend analysis and process monitoring for the mean absolute error of the active content were conducted. The combined use of DoE, drift analysis, and SPC enabled trend analysis, ensuring that both current and external validation data met acceptance criteria. Out-of-specification and process control limits were determined, providing valuable insights into the models performance and overall reliability. This comprehensive approach allowed for robust hyperparameter optimization and effective management of model lifecycle, crucial in achieving accurate and dependable predictions in various real-world applications. Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s). -
Machine learning-enhanced heat and mass transfer study of elliptic motion in piezoelectric thermoelastic plates using Green-Naghdi III and three-phase-lag theories
Rayleigh-type surface waves in piezoelectric (PE) solids are pivotal for acoustic sensors, microelectromechanical systems (MEMS), and non-destructive evaluation. However, classical thermoelastic models fail under high heat flux due to the assumption of infinite thermal signal speeds, which limits their accuracy in coupled thermo-mechanical systems. To capture finite-speed and memory-dependent thermal effects, the Rayleigh wave propagation in a transversely isotropic (TI) PE half-space using generalized theories (such as Green-Naghdi type III (GN-III) and three-phase-lag (TPL)) is studied in this paper. The analytical formulation under varied electrical and thermal boundary conditions has been obtained. Secular equations are derived to characterize phase velocity, attenuation, and specific energy loss. A regression-based machine learning (ML) surrogate model is trained by using an analytical dataset to provide rapid predictions of wave parameters. Additionally, a confusion matrix classifier is applied to identify boundary conditions from simulated wave response features. The results demonstrated that the phase velocity increases with inclination angle and stabilizes with wave number, whereas attenuation and specific loss vary strongly by boundary condition (e.g., minimal in shorted-isothermal cases). The ML surrogate successfully reconstructed analytical predictions with minimal residual error, and the confusion matrix demonstrates accurate classification performance and validates the diagnostic potential of the framework. The novelty of this paper lies in integrating dual thermoelastic theories with machine learning, merging mechanics, heat transfer, and intelligent computing. These findings enable enhanced SAW sensor designs for precise gas/chemical detection, low-loss NDE tools for aerospace composite defect identification, and real-time diagnostics in biomedical ultrasonics for clearer imaging and efficient energy harvesting. 2026 Elsevier Ltd -
Machine Learning's Transformative Role in Human Activity Recognition Analysis
Human action recognition (HAR) is a burgeoning field of computer vision that seeks to automatically understand and classify the intricate movements performed by humans. From the graceful leaps of a ballerina to the decisive strides of a surgeon, HAR aims to decipher the language of motion, unlocking a plethora of potential applications. This abstract delves into the core of HAR, highlighting its key challenges and promising avenues for advancement. We begin by outlining the various modalities used for action recognition, such as RGB videos, depth sensors, and skeletal data, each offering unique perspectives on the human form. Next, we delve into the diverse set of algorithms employed for HAR, ranging from traditional machine learning techniques to the burgeoning realm of deep learning. We explore the strengths and limitations of each approach, emphasizing the crucial role of feature extraction and model selection in achieving accurate recognition. Challenges in Human Action Recognition (HAR), such as intra-class variations, inter-class similarities, and environmental factors. Ongoing efforts include robust feature development and contextual integration. The paper envisions HAR's future impact on healthcare, robotics, video surveillance, and augmented reality, presenting an invitation to explore the transformative world of human action recognition and its potential to enhance our interaction with technology. 2024 IEEE. -
Machine learningbased approaches for enhancing human resource management using automated employee performance prediction systems
Purpose: This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance. Design/methodology/approach: In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employees performance meets expectations or needs improvement. Findings: All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce. Research limitations/implications: The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the models effectiveness across various contexts. Practical implications: The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment. Social implications: Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance. Originality/value: This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach. 2024, Emerald Publishing Limited. -
Machine learningbased approaches for enhancing human resource management using automated employee performance prediction systems
Purpose This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance. Design/methodology/approach In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employees performance meets expectations or needs improvement. Findings All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce. Research limitations/implications The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the models effectiveness across various contexts. Practical implications The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment. Social implications Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance. Originality/value This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach. 2024 Emerald Publishing Limited -
Machine Learningcloud-Based Approach to Identify and Classify Disease
The term "Internet of Things"(IoT) describes the process of creating and modeling web-related physical objects across computing systems. IoT-based healthcare applications have offered multiple real-time products and benefits in recent years. For millions of people, these programmers provide hospitalization can get regular medical records and healthy lives. The introduction of IoT devices in the health sector has several technological developments. This study uses the IoT to construct a disease diagnostic system. Wearable sensors in this system initially monitor the patient's sympathy impulses. The impulses are then sent by a network environment to a server. In addition, a new hybrid approach to evaluation decision-making was presented as part of this research. This technique starts with the development of a set of features of the patient's pulses. Based on a learning approach qualifications are neglected. A fuzzy neural model was used as a diagnostic tool. A specific diagnosis of a particular ailment, such as the diagnosis of a patient's normal and abnormal pulse or the assessment of insulin issues, would be modeled to assess this technology. 2022 IEEE.