Disease Diagnosis Model Research Articles

A comparative assessment of artificial intelligence models used for early prediction and evaluation of chronic kidney disease

Chronic Kidney Disease (CKD) is one of the most prevalent and fatal diseases influencing people on a larger that remains dormant until irreversible damage has been done to an individual’s kidney. Progression of CKD is related to a variety of great complications, including increased incidence of various disorders, anemia, hyperlipidemia, nerve damage, pregnancy complication, and even complete kidney failure. Millions of people die from this disease every year. Diagnosing CKD is a cumbersome task as no major symptoms can be used as a benchmark to detect the disease. In cases when diagnosis persists, some results may be interpreted incorrectly. This study proposes using a deep neural network-based Multi-Layer Perceptron Classifier to diagnose CKD in patients. The model was trained using data from 400 people and considered various symptoms and signs, including age, blood sugar, red blood cell count, etc. Experiments reveal that the proposed model achieves perfect testing accuracy in classification tasks. Our goal is to facilitate introducing Deep Learning approaches to learning from the dataset attribute reports and accurately detecting CKD. The paper’s primary contribution is a Deep Neural Network model for chronic kidney disease diagnosis that achieves 100% accuracy, outperforming standard machine learning models such as support vector machines and naive Bayes classifiers. This paper provides a detailed explanation of the multi-layer perceptron classifier, which uses the deep neural network provided by the PyTorch library as its basis. Neural models can be a better alternative for adaption techniques for classifying chronic kidney disease Because they can handle non-linearity in the data, compute complex data heaps fetched from datasets, and adapt and learn on their own about the key information using the layers of neurons present in the structure.

A precise model for accurate rice disease diagnosis: a transfer learning approach

Rice being a principal cereal, is consumed by a large portion of the world's population as a staple food. Like other plants, it is susceptible to a variety of diseases associated with themselves which can affect greatly the quantity and quality of yield. It is essential to detect the disease in its early stage and take appropriate treatment measures to ensure proper growth of the plant as well as maximum quality yield. Detection of the diseased plants through the naked eye seems to be an impossible task taking into consideration the large range of leaf diseases and the vast areas of farmlands. Technological advancement in the areas of machine learning, computer vision, and IoT has led to the development of automated systems for the detection of diagnosis of plant diseases from the image of diseased leaves. Though machine learning (ML) based solutions are available for this task, still there is potential for further improvement in the automated systems build on transfer learning approaches based on different CNN architectures. This paper proposes a transfer learning-based AlexNet model for automatic detection and diagnosis task. Three of the most common rice plant disease namely bacterial leaf blight, brown spot, and leaf smut which are very difficult to distinguish by the naked eye have been considered for investigation. The dataset is obtained from the 'Kaggle' website. The proposed AlexNet model performs significantly well for the detection and classification task on 'Kaggle' dataset in terms of accuracy, precision, recall, F1 score, and kappa coefficient in comparison with other ML and transfer learning-based approaches reported in the literature for the same task on same or different datasets.

Simulated Annealing with Deep Learning Based Tongue Image Analysis for Heart Disease Diagnosis

Tongue image analysis is an efficient and non-invasive technique to determine the internal organ condition of a patient in oriental medicine, for example, traditional Chinese medicine (TCM), Japanese traditional herbal medicine, and traditional Korean medicine (TKM). The diagnosis procedure is mainly based on the expert's knowledge depending upon the visual inspection comprising color, substance, coating, form, and motion of the tongue. But conventional tongue diagnosis has limitations since the procedure is inconsistent and subjective. Therefore, computer-aided tongue analyses have a greater potential to present objective and more consistent health assessments. This manuscript introduces a novel Simulated Annealing with Transfer Learning based Tongue Image Analysis for Disease Diagnosis (SADTL-TIADD) model. The presented SADTL-TIADD model initially pre-processes the tongue image to improve the quality. Next, the presented SADTL-TIADD technique employed an EfficientNet-based feature extractor to generate useful feature vectors. In turn, the SA with the ELM model enhances classification efficiency for disease detection and classification. The design of SA-based parameter tuning for heart disease diagnosis shows the novelty of the work. A wide-ranging set of simulations was performed to ensure the improved performance of the SADTL-TIADD algorithm. The experimental outcomes highlighted the superior of the presented SADTL-TIADD system over the compared methods with maximum accuracy of 99.30%.

Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

Recently, Internet of Medical Things (IoMT) has gained considerable attention to provide improved healthcare services to patients. Since earlier diagnosis of brain tumor (BT) using medical imaging becomes an essential task, automated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning models. With this motivation, this paper introduces a novel IoMT and cloud enabled BT diagnosis model, named IoMTC-HDBT. The IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging (MRI) brain images and transmit them to the cloud server. Besides, adaptive window filtering (AWF) based image preprocessing is used to remove noise. In addition, the cloud server executes the disease diagnosis model which includes the sparrow search algorithm (SSA) with GoogleNet (SSA-GN) model. The IoMTC-HDBT model applies functional link neural network (FLNN), which has the ability to detect and classify the MRI brain images as normal or abnormal. It finds useful to generate the reports instantly for patients located in remote areas. The validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is carried out interms of sensitivity, accuracy, and specificity. The experimentation outcome pointed out the betterment of the proposed model with the accuracy of 0.984.

Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

Melanoma remains a serious illness which is a common form of skin cancer. Since the earlier detection of melanoma reduces the mortality rate, it is essential to design reliable and automated disease diagnosis model using dermoscopic images. The recent advances in deep learning (DL) models find useful to examine the medical image and make proper decisions. In this study, an automated deep learning based melanoma detection and classification (ADL-MDC) model is presented. The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma. The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage. Besides, the k-means clustering technique is applied for the image segmentation process. In addition, Adagrad optimizer based Capsule Network (CapsNet) model is derived for effective feature extraction process. Lastly, crow search optimization (CSO) algorithm with sparse autoencoder (SAE) model is utilized for the melanoma classification process. The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance. A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects. The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.

Chronic disease diagnosis model based on convolutional neural network and ensemble learning method.

Chronic diseases have become one of the main causes of premature death all around the world in recent years. The diagnosis of chronic diseases is time-consuming and costly. Therefore, timely diagnosis and prediction of chronic diseases are very necessary. In this paper, a new method for chronic disease diagnosis is proposed by combining convolutional neural network (CNN) and ensemble learning. This method utilizes random forest (RF) as the base classifier to improve classification performance and diagnostic accuracy, and then combines AdaBoost to successfully replace the Softmax layer of CNN to generate multiple accurate base classifiers while determining their optimal attributes, achieving high-quality classification and prediction of chronic diseases. To verify the effectiveness of the proposed method, real-world Electronic Medical Records dataset (C-EMRs) was used for experimental analysis. The results show that compared with other traditional machine learning methods such as CNN, K-Nearest Neighbor, and RF, the proposed method can effectively improve the accuracy of diagnosis and reduce the occurrence of missed diagnosis and misdiagnosis. This study will provide effective information for the diagnosis of chronic diseases, assist doctors in making clinical decisions, develop targeted intervention measures, and reduce the probability of misdiagnosis.

VGBNet: a disease diagnosis model based on local and global information fusion

There are significant differences in the data volume of different types of diseases in the electronic medical record (EMR) data. In addition, the mainstream auxiliary diagnosis and prediction models have two shortcomings. They either only pay attention to the local features of medical records and ignore the global features, or only pay attention to the global features and ignore the local features. In response to these problems, we propose a method of fusion random resampling to balance the data set, Using graph convolutional neural network (GCN) to extract global features, combined with a bidirectional self-attention network (BERT), a VGBNet model is proposed to link local and global features to achieve diagnosis and prediction of diseases. A large number of experiments show that compared with the state-of-the-art models, the VGBNet model has improved F1 value and accuracy. This is of great significance to the precise auxiliary diagnosis.

VGBNet: a disease diagnosis model based on local and global information fusion

VGBNet: a disease diagnosis model based on local and global information fusion

Sequential Backward Feature Selection Method Based on Stacking Framework

Aiming at the problems of noise and irrelevant feature filtering in data processing, a feature selection method based on the stacking framework is proposed. Use the K-Fold cross-validation method to train and save DNN and SVM-based learners. The prediction results of the base learners are used as the input of the meta-learner, and the logistic regression learning model is trained and saved; comprehensively analyze the correlation coefficients of the fully connected neural network weight matrix and support vector machine, According to the learning results of the meta-learner model, assign different weights to each base learner, calculate the influence factors of each feature, and call the sequence backward search algorithm (SBS) to generate the optimal feature subset. In the experimental stage, a disease diagnosis model was constructed based on the open data set of heart disease research on the Kaggle website, and Stacking-SBS was called to generate the optimal feature subset in the feature space, and the performance comparison experiment of the diagnostic model before and after feature selection was performed, and the method was improved with information. (IG), Chi-square test (Chi) and correlation-based feature selection method (CFS) are compared. The results show that the application of this method can not only reduce model training time, but also significantly improve the model's recall rate and F1 value. In addition, this method is significantly better than the other three feature selection methods in terms of performance improvement. Finally, the open data set of cardiovascular research on the Kaggle website is used to verify the generalization ability of Stacking-SBS. The experimental results show that this method can also significantly improve the performance of the disease diagnosis model.

Efficient Model for Coronary Artery Disease Diagnosis: A Comparative Study of Several Machine Learning Algorithms.

Background In today's industrialized world, coronary artery disease (CAD) is one of the leading causes of death, and early detection and timely intervention can prevent many of its complications and eliminate or reduce the resulting mortality. Machine learning (ML) methods as one of the cutting-edge technologies can be used as a suitable solution in diagnosing this disease. Methods In this study, different ML algorithms' performances were compared for their effectiveness in developing a model for early CAD diagnosis based on clinical examination features. This applied descriptive study was conducted on 303 records and overall 26 features, of which 26 were selected as the target features with the advice of several clinical experts. In order to provide a diagnostic model for CAD, we ran most of the most critical classification algorithms, including Multilayer Perceptron (MLP), Support Vector Machine (SVM), Logistic Regression (LR), J48, Random Forest (RF), K-Nearest Neighborhood (KNN), and Naive Bayes (NB). Seven different classification algorithms with 26 predictive features were tested to cover all feature space and reduce model error, and the most efficient algorithms were identified by comparison of the results. Results Based on the compared performance metrics, SVM (AUC = 0.88, F-measure = 0.88, ROC = 0.85), and RF (AUC = 0.87, F-measure = 0.87, ROC = 0.91) were the most effective ML algorithms. Among the algorithms, the KNN algorithm had the lowest efficiency (AUC = 0.81, F-measure = 0.81, ROC = 0.77). In the diagnosis of coronary artery disease, machine learning algorithms have played an important role. Proposed ML models can provide practical, cost-effective, and valuable support to doctors in making decisions according to a good prediction. Discussion. It can become the basis for developing clinical decision support systems. SVM and RF algorithms had the highest efficiency and could diagnose CAD based on patient examination data. It is suggested that further studies be performed using these algorithms to diagnose coronary artery disease to obtain more accurate results.

Practical Privacy Preserving-Aided Disease Diagnosis with Multiclass SVM in an Outsourced Environment

With the rapid development of cloud computing and machine learning, using outsourced stored data and machine learning model for training and online-aided disease diagnosis has a great application prospect. However, training and diagnosis in an outsourced environment will cause serious challenges to the privacy of data. At present, many scholars have proposed privacy preserving machine learning schemes and made a lot of progress, but there are still great challenges in security and low client load. In this paper, we propose a complete privacy preserving outsourced multiclass SVM training and aided disease diagnosis scheme. We design some efficient basic operation algorithms for encrypted data. Then, we design an efficient and privacy preserving SVM model training protocol using the basic operation algorithms. We propose a secure maximum finding algorithm and secure comparison algorithm. Then, we design an efficient online-aided disease diagnosis scheme based on the BFV cryptosystem and blinding technique. Detailed security analysis proves that our scheme can protect the privacy of each participant. The experimental results illustrate that our proposed scheme significantly reduces the computation overhead compared with the previous similar works. Our proposed scheme completes most of the operations of aided disease diagnosis by the cloud servers and the client only needs to complete a small amount of encryption and decryption operations. The overall computation overhead is 0.175 s, and the efficiency of online aided disease diagnosis is improved by 85.4%. At the same time, our proposed scheme provides multiclass diagnosis results, which can better assist doctors in their treatment.

A Hybrid Feature Reduction Approach for Medical Decision Support System

Feature reduction is essential at the preprocessing stage of designing any reliable and fast disease diagnosis model. Addressing the limitations like disease specificity, information loss, and operating NP problem in polynomial time, this paper introduces a two-step hybrid feature selection approach to identify a subset of most relevant and contributing features of each medical dataset for constructing diagnostic model. The concept of information gain is used in Step I to select the informative features, whereas a correlation coefficient-based approach is employed in Step II to retain the informative features possessing much dependency with class attribute but less dependency among the non-class attributes. In particular, both the approaches are sequentially fused to select approximately optimal features in order to construct better classification model in terms of performance and time. The optimal threshold criteria are decided to choose the most appropriate features from the datasets. The effectiveness of the proposed approach is assessed using six individual competent learners and one ensemble learner over seventeen disease datasets of smaller to larger dimensions. The empirical results indicate that the proposed approach improves the performance over the datasets after feature selection, reducing considerable amount of irrelevant and redundant data.

DEEP-FEL: Decentralized, Efficient and Privacy-Enhanced Federated Edge Learning for Healthcare Cyber Physical Systems

The rapid development of Internet of Things (IoT) stimulates the innovation for the health-related devices such as remote patient monitoring, connected inhalers and ingestible sensors. Simultaneously, with the aid of numerous equipments, a great number of collected data can be used for disease prediction or diagnosis model establishment. However, the potential patient data leak will also bring privacy and security issues in the interaction period. To deal with these existing issues, we propose a decentralized, efficient, and privacy-enhanced federated edge learning system called DEEP-FEL, which enables medical devices in different institutions to collaboratively train a global model without raw data mutual exchange. Firstly, we design a hierarchical ring topology to alleviate centralization of the conventional training framework, and formulate the ring construction as an optimization problem, which can be solved by an efficient heuristic algorithm. Subsequently, we design an efficient parameter aggregation algorithm for distributed medical institutions to generate a new global model, and the total amount of data transmitted by N nodes is only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2/N$</tex-math></inline-formula> times that of traditional algorithm. In addition, data security among different medical institutions is enhanced by adding artificial noise to the edge model. Finally, experimental results on three medical datasets demonstrate the superiority of our system.

Developing an Integrated Model for Heart Disease Diagnosis (IM-HDD) using ensemble classification methods

In present scenario, Heart Disease has become the vital cause of mortality and diagnosis of heart diseases is a great confrontation in the field of medical data analysis. Data Mining is an efficient technique for processing and analyzing larger databases for deriving hidden knowledge appropriately. Hence, it is incorporated in medical data analysis for assisting in effective decision making and disease predictions. With that concern, this paper concentrates on framing an Integrated Model for Heart Disease Diagnosis (IM-HDD) using the advanced data mining conceits. The model considers the significant features of patient data that are available in benchmark datasets. Here, the main objective of the proposed model is to enhance the classification accuracy of patient data on classes under NORMAL and ABNORMAL. For enhancing the classification accuracy, the proposed integrated model utilizes the algorithms such as Decision Tree Algorithm, Naive Baye’s Classification and Ensemble Classifiers called Random Forest and Bagging. Further, performance evaluation is performed for analyzing the proposed work. For that, images from UCI repository are utilized and the comparative analysis shows that the proposed work produces better results than the existing models compared.

DKPNet41: Directed knight pattern network-based cough sound classification model for automatic disease diagnosis.

ProblemCough-based disease detection is a hot research topic for machine learning, and much research has been published on the automatic detection of Covid-19. However, these studies are useful for the diagnosis of different diseases.AimIn this work, we collected a new and large (n=642 subjects) cough sound dataset comprising four diagnostic categories: ‘Covid-19’, ‘heart failure’, ‘acute asthma’, and ‘healthy’, and used it to train, validate, and test a novel model designed for automatic detection.MethodThe model consists of four main components: novel feature generation based on a specifically directed knight pattern (DKP), signal decomposition using four pooling methods, feature selection using iterative neighborhood analysis (INCA), and classification using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation. Multilevel multiple pooling decomposition combined with DKP yielded 41 feature vectors (40 extracted plus one original cough sound). From these, the ten best feature vectors were selected. Based on each vector's misclassification rate, redundant feature vectors were eliminated and then merged. The merged vector's most informative features automatically selected using INCA were input to a standard kNN classifier.ResultsThe model, called DKPNet41, attained a high accuracy of 99.39% for cough sound-based multiclass classification of the four categories.ConclusionsThe results obtained in the study showed that the DKPNet41 model automatically and efficiently classifies cough sounds for disease diagnosis.

Efficiency of Disease and Disease Activity Diagnosis Models of Systemic Lupus Erythematosus Based on Protein Array Analysis.

Background Systemic lupus erythematosus (SLE) has become increasingly common in the clinic and requires complicated evidence of both clinical manifestations and laboratory examinations. Additionally, the assessment and monitoring of lupus disease activity are challenging. We hope to find efficient biomarkers and establish diagnostic models of SLE. Materials and Methods We detected and quantified 40 proteins using a quantitative protein array of 76 SLE patients and 21 healthy controls, and differentially expressed proteins were screened out by volcano plot. Logistic regression analysis was used to recognize biomarkers that could be enrolled in the disease diagnosis model and disease activity diagnosis model, and a receiver operating characteristic (ROC) curve was drawn to evaluate the efficiency of the model. A nomogram was depicted for convenient and visualized application of our models in the clinic. Decision curves and clinical impact curves were also plotted to validate our models. Results The protein levels of TNF RII, BLC, TNF RI, MIP-1b, eotaxin, MIG, MCSF, IL-8, MCP-1, and IL-10 showed significant differences between patients with SLE and healthy controls. TNF RII and MIP-1b were included in the SLE diagnosis model with logistic regression analysis, and the value of the area under the ROC curve (AUC) was 0.914 (95% confidence interval (CI), 0.859-0.969). TNF RII, BLC, and MIP-1b were enrolled in the disease activity diagnosis model, and the AUC value was 0.823 (95% CI 0.729-0.916). Both of the models that we established showed high efficiency. Additionally, the three protein biomarkers contained in the disease activity distinguish model provided additional benefit to conventional biomarkers in predicting active lupus. Conclusions The disease diagnosis model and disease activity diagnosis model that we developed based on protein array chip results showed high efficiency in differentiating patients with SLE from healthy controls and recognizing SLE patients with high disease activity, and they have also been validated. This implied that they might greatly benefit clinical decisions and the treatment of SLE.

Modified local binary patterns based feature extraction and hyper parameters tuned attention segmental recurrent neural network classifier using flamingo search optimization algorithm for disease diagnosis model

SummaryIn this article, the modified local binary patterns based feature extraction and hyper parameters tuned attention segmental recurrent neural network classifier with flamingo search optimization algorithm (MLBPFE‐ASRNNFSOAC‐DDM) is proposed for the disease diagnosis model. Here, breast cancer, diabetic, chronic kidney diseases diagnosis model is implemented. Initially, images are considered as dataset for disease diagnosis, which is given to the altered phase preserving dynamic range compression (APPDRC) scheme for preprocessing process. This APPDRC is used to preserve local features for boundary detection, thus; recovers image quality. Then the morphological, grayscale statistical and Haralick texture features are taken from preprocessed image with the help of modified local binary pattern process. The extracted features are given with attention segmented recurrent neural network (ASRNN) for classification. Then the weight parameters of ASRNN classifier are optimized by flamingo search optimization algorithm (FSOA), which increases the classification accuracy. This simulation process is accomplished at MATLAB platform. The proposed method, during Diabetic Diagnosis Model attains higher accuracy 16.4%, 21.45%, 30.38%, and 21.01% compared with the existing methods like FE‐ResNetV2DNNMSOC‐DDM and CMVHHO‐DKMLC‐DDM, CNN‐CAD‐DDM, and AD‐CFDRI‐SDL, respectively. During breast cancer Diagnosis Model attains higher accuracy 27.3%, 20.56%, 31.34%, and 25.13% compared with the existing methods like CMVHHO‐DKMLC‐BCM and SVMFE‐OPFPSOC‐BCM, MLPNN‐CNN‐BCM, and DCNN‐EL‐BCM, respectively.

Retraction Note to: An optimal artificial neural network based big data application for heart disease diagnosis and classification model

Retraction Note to: An optimal artificial neural network based big data application for heart disease diagnosis and classification model

Tomato disease and pest diagnosis method based on the Stacking of prescription data

Crop prescription data contains an extensive amount of information on crops, environment and pests, and has notable diagnostic capabilities. At present, there is lack of feasible methods for efficiently mining crop prescription data to perform accurate diagnoses. In view of the above problems, the purpose of our study is to mine prescription data information and assist the accurate diagnosis of crop diseases. In this paper, six tomato diseases and pests, namely, the tomato virus disease, tomato late blight, tomato gray mold, aphid, thrips and whiteflies, were explored to construct a diagnosis model based on prescription data mining. Original prescription data was subjected to pre-processing, text labeling and one-hot coding. The recursive feature elimination (RFE) method was then employed to extract 37 key features relating to crop diseases and pests from original 50 features. We constructed a tomato disease and pest diagnosis model based on two-stage Stacking ensemble learning to improve the diagnosis accuracy. The experimental results demonstrated the proposed diagnosis model in this paper exhibits a slightly superior performance compared to the best model (LGBM) among ten diagnosis models. The optimal Stacking model is composed of two layers: base-classifiers including GDBT, XGBoost and LGBM, and meta-classifier RF. The diagnosis accuracy of the proposed model for the tomato virus disease reached 94.84%, with an F1-score of 95.98% and overall accuracy of 80.36%. It also performed well on the multi-classification metrics: Macro avg (Precision: 76.55%, Recall: 78.17%, F1-score: 77.05%) and Weighted avg (Precision: 80.96%, Recall: 80.36%, F1-score: 80.50%). Moreover, following feature selection, the Stacking-based diagnosis model can reduce the running time by 12.08% with unchanged diagnosis accuracy. The proposed diagnosis model meets the real-world diagnosis requirements. This work provides new research concepts and a methodological foundation for future crop disease and pest diagnosis.

An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease.

Lyme disease is one of the most common vector-borne infections. It typically causes cardiac illnesses, neurologic illnesses, musculoskeletal disorders, and dermatologic conditions. However, most of the time, it is poorly diagnosed due to many similarities with other diseases such as drug rash. Given the potentially serious consequences of unnecessary antimicrobial treatments, it is essential to understand frequent and uncommon diagnoses that explain symptoms in this population. Recently, deep learning models have been used for the diagnosis of various rash-related diseases. However, these models suffer from overfitting and color variation problems. To overcome these problems, an efficient stacked deep transfer learning model is proposed that can efficiently distinguish between patients infected with Lyme (+) or infected with other infections. 2nd order edge-based color constancy is used as a preprocessing approach to reduce the impact of multisource light from images acquired under different setups. The AlexNet pretrained learning model is used for building the Lyme disease diagnosis model. To prevent overfitting, data augmentation techniques are also used to augment the dataset. In addition, 5-fold cross-validation is also used. Comparative analysis indicates that the proposed model outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and area under the curve.