An Efficient Stacked Ensemble Model for Heart Disease Detection and Classification

Cardiac disease is a chronic condition that impairs the heart’s functionality.It includes conditions such as coronary artery disease,heart failure,arrhythmias,and valvular heart disease.These conditions can lead to serious complications and even be life-threatening if not detected and managed in time.Researchers have utilized Machine Learning(ML)and Deep Learning(DL)to identify heart abnormalities swiftly and consistently.Various approaches have been applied to predict and treat heart disease utilizing ML and DL.This paper proposes a Machine and Deep Learning-based Stacked Model(MDLSM)to predict heart disease accurately.ML approaches such as eXtreme Gradient Boosting(XGB),Random Forest(RF),Naive Bayes(NB),Decision Tree(DT),and KNearest Neighbor(KNN),along with two DL models:Deep Neural Network(DNN)and Fine Tuned Deep Neural Network(FT-DNN)are used to detect heart disease.These models rely on electronic medical data that increases the likelihood of correctly identifying and diagnosing heart disease.Well-known evaluation measures(i.e.,accuracy,precision,recall,F1-score,confusion matrix,and area under the Receiver Operating Characteristic(ROC)curve)are employed to check the efficacy of the proposed approach.Results reveal that the MDLSM achieves 94.14%prediction accuracy,which is 8.30%better than the results from the baseline experiments recommending our proposed approach for identifying and diagnosing heart disease.

Chest Radiographs Based Pneumothorax Detection Using Federated Learning

Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse,causing air to enter the pleural cavity,the area close to the lungs and chest wall.The most persistent disease,as well as one that necessitates particular patient care and the privacy of their health records.The radiologists find it challenging to diagnose pneumothorax due to the variations in images.Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems.However,it is challenging to employ it in the medical field due to privacy issues and a lack of data.To address this issue,a federated learning framework based on an Xception neural network model is proposed in this research.The pneumothorax medical image dataset is obtained from the Kaggle repository.Data preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model’s performance.Min-max normalization technique is used to normalize the data,and the features are extracted from chest Xray images.Then dataset converts into two windows to make two clients for local model training.Xception neural network model is trained on the dataset individually and aggregates model updates from two clients on the server side.To decrease the over-fitting effect,every client analyses the results three times.Client 1 performed better in round 2 with a 79.0%accuracy,and client 2 performed better in round 2 with a 77.0%accuracy.The experimental result shows the effectiveness of the federated learning-based technique on a deep neural network,reaching a 79.28%accuracy while also providing privacy to the patient’s data.

CD-FL:Cataract Images Based Disease Detection Using Federated Learning

A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over time.Automatic cataract prediction based on various imaging technologies has been addressed recently,such as smartphone apps used for remote health monitoring and eye treatment.In recent years,advances in diagnosis,prediction,and clinical decision support using Artificial Intelligence(AI)in medicine and ophthalmology have been exponential.Due to privacy concerns,a lack of data makes applying artificial intelligence models in the medical field challenging.To address this issue,a federated learning framework named CDFL based on a VGG16 deep neural network model is proposed in this research.The study collects data from the Ocular Disease Intelligent Recognition(ODIR)database containing 5,000 patient records.The significant features are extracted and normalized using the min-max normalization technique.In the federated learning-based technique,the VGG16 model is trained on the dataset individually after receiving model updates from two clients.Before transferring the attributes to the global model,the suggested method trains the local model.The global model subsequently improves the technique after integrating the new parameters.Every client analyses the results in three rounds to decrease the over-fitting problem.The experimental result shows the effectiveness of the federated learning-based technique on a Deep Neural Network(DNN),reaching a 95.28%accuracy while also providing privacy to the patient’s data.The experiment demonstrated that the suggested federated learning model outperforms other traditional methods,achieving client 1 accuracy of 95.0%and client 2 accuracy of 96.0%.

Ensemble Learning for Fetal Health Classification

Cardiotocography(CTG)represents the fetus’s health inside the womb during labor.However,assessment of its readings can be a highly subjective process depending on the expertise of the obstetrician.Digital signals from fetal monitors acquire parameters(i.e.,fetal heart rate,contractions,acceleration).Objective:This paper aims to classify the CTG readings containing imbalanced healthy,suspected,and pathological fetus readings.Method:We perform two sets of experiments.Firstly,we employ five classifiers:Random Forest(RF),Adaptive Boosting(AdaBoost),Categorical Boosting(CatBoost),Extreme Gradient Boosting(XGBoost),and Light Gradient Boosting Machine(LGBM)without over-sampling to classify CTG readings into three categories:healthy,suspected,and pathological.Secondly,we employ an ensemble of the above-described classifiers with the oversamplingmethod.We use a random over-sampling technique to balance CTG records to train the ensemble models.We use 3602 CTG readings to train the ensemble classifiers and 1201 records to evaluate them.The outcomes of these classifiers are then fed into the soft voting classifier to obtain the most accurate results.Results:Each classifier evaluates accuracy,Precision,Recall,F1-scores,and Area Under the Receiver Operating Curve(AUROC)values.Results reveal that the XGBoost,LGBM,and CatBoost classifiers yielded 99%accuracy.Conclusion:Using ensemble classifiers over a balanced CTG dataset improves the detection accuracy compared to the previous studies and our first experiment.A soft voting classifier then eliminates the weakness of one individual classifier to yield superior performance of the overall model.

An Ensemble Machine Learning Technique for Stroke Prognosis

Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the brain.It has a tremendous impact on every aspect of life since it is the leading global factor of disability and morbidity.Strokes can range from minor to severe(extensive).Thus,early stroke assessment and treatment can enhance survival rates.Manual prediction is extremely time and resource intensive.Automated prediction methods such as Modern Information and Communication Technologies(ICTs),particularly those inMachine Learning(ML)area,are crucial for the early diagnosis and prognosis of stroke.Therefore,this research proposed an ensemble voting model based on three Machine Learning(ML)algorithms:Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Light Gradient Boosting Machine(LGBM).We apply data preprocessing to manage the outliers and useless instances in the dataset.Furthermore,to address the problem of imbalanced data,we enhance the minority class’s representation using the Synthetic Minority Over-Sampling Technique(SMOTE),allowing it to engage in the learning process actively.Results reveal that the suggested model outperforms existing studies and other classifiers with 0.96%accuracy,0.97%precision,0.97%recall,and 0.96%F1-score.The experiment demonstrates that the proposed ensemble voting model outperforms state-of-the-art and other traditional approaches.

DeepCNN:Spectro-temporal feature representation for speech emotion recognition

Speech emotion recognition(SER)is an important research problem in human-computer interaction systems.The representation and extraction of features are significant challenges in SER systems.Despite the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive fields.To mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based transformer.Two parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel CNNs.The learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification layer.This fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature map.The Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech emotions.With efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset respectively.The proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

Automatic Classification of Superimposed Modulations for 5G MIMO Two-Way Cognitive Relay Networks

To promote reliable and secure communications in the cognitive radio network,the automatic modulation classification algorithms have been mainly proposed to estimate a single modulation.In this paper,we address the classification of superimposed modulations dedicated to 5G multipleinput multiple-output(MIMO)two-way cognitive relay network in realistic channels modeled with Nakagami-m distribution.Our purpose consists of classifying pairs of users modulations from superimposed signals.To achieve this goal,we apply the higher-order statistics in conjunction with the Multi-BoostAB classifier.We use several efficiency metrics including the true positive(TP)rate,false positive(FP)rate,precision,recall,F-Measure and receiver operating characteristic(ROC)area in order to evaluate the performance of the proposed algorithm in terms of correct superimposed modulations classification.Computer simulations prove that our proposal allows obtaining a good probability of classification for ten superimposed modulations at a low signal-to-noise ratio,including the worst case(i.e.,m=0.5),where the fading distribution follows a one-sided Gaussian distribution.We also carry out a comparative study between our proposal usingMultiBoostAB classifier with the decision tree(J48)classifier.Simulation results show that the performance of MultiBoostAB on the superimposed modulations classifications outperforms the one of J48 classifier.In addition,we study the impact of the symbols number,path loss exponent and relay position on the performance of the proposed automatic classification superimposed modulations in terms of probability of correct classification.