This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naive bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.

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Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare

Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).

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Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwatch and heart monitor device that is attached to the patient monitors the blood pressure and electrocardiogram (ECG). The MDCNN is utilized for classifying the received sensor data into normal and abnormal. The performance of the system is analyzed by comparing the proposed MDCNN with existing deep learning neural networks and logistic regression. The results demonstrate that the proposed MDCNN based heart disease prediction system performs better than other methods. The proposed method shows that for the maximum number of records, the MDCNN achieves an accuracy of 98.2 which is better than existing classifiers.

An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier

Heart disease, one of the major causes of mortality worldwide, can be mitigated by early heart disease diagnosis. A clinical decision support system (CDSS) can be used to diagnose the subjects' heart disease status earlier. This study proposes an effective heart disease prediction model (HDPM) for a CDSS which consists of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and eliminate the outliers, a hybrid Synthetic Minority Over-sampling Technique-Edited Nearest Neighbor (SMOTE-ENN) to balance the training data distribution and XGBoost to predict heart disease. Two publicly available datasets (Statlog and Cleveland) were used to build the model and compare the results with those of other models (naive bayes (NB), logistic regression (LR), multilayer perceptron (MLP), support vector machine (SVM), decision tree (DT), and random forest (RF)) and of previous study results. The results revealed that the proposed model outperformed other models and previous study results by achieving accuracies of 95.90% and 98.40% for Statlog and Cleveland datasets, respectively. In addition, we designed and developed the prototype of the Heart Disease CDSS (HDCDSS) to help doctors/clinicians diagnose the patients'/subjects' heart disease status based on their current condition. Therefore, early treatment could be conducted to prevent the deaths caused by late heart disease diagnosis.

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HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System

Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart disease is important for heart failure prevention and treatment. The diagnosis of heart disease through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart disease, noninvasive-based methods such as machine learning are reliable and efficient. In the proposed study, we developed a machine-learning-based diagnosis system for heart disease prediction by using heart disease dataset. We used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and seven classifiers performance evaluation metrics such as classification accuracy, specificity, sensitivity, Matthews’ correlation coefficient, and execution time. The proposed system can easily identify and classify people with heart disease from healthy people. Additionally, receiver optimistic curves and area under the curves for each classifier was computed. We have discussed all of the classifiers, feature selection algorithms, preprocessing methods, validation method, and classifiers performance evaluation metrics used in this paper. The performance of the proposed system has been validated on full features and on a reduced set of features. The features reduction has an impact on classifiers performance in terms of accuracy and execution time of classifiers. The proposed machine-learning-based decision support system will assist the doctors to diagnosis heart patients efficiently.

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A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

The accurate diagnosis of Parkinson disease specifically in its initial stages is extremely complex and time consuming. Thus the accurate and efficient diagnosis of Parkinson disease has been a significant challenge for medical experts and researchers. In order to tackle the accurate diagnosis of Parkinson disease issue we proposed machine learning and deep neural networks based non-invasive prediction system for accurately and on time diagnosis of Parkinson disease. In the development of the system machine learning predictive models such as support vector machine, logistic regression and deep neural network were used for people with Parkinson disease and healthy people classification. The data set was splits into 70% for training purpose and 30% for testing. Furthermore, performance evaluation metrics such as classification accuracy, sensitivity, specificity and Matthews's correlation coefficient were utilized for model performance evaluation. The Parkinson disease dataset of 23 attributes and 195 instances available on UCI machine learning repository was used for testing of the proposed system. Through our experimental results analysis shows that the proposed system classified the Parkinson disease and healthy people effectively. We also investigated that deep neural performance of classification was excellent as compared to traditional machines learning classifiers. These finding suggest that the proposed diagnosis system could be used to accurately predict Parkinson disease.

Comparative Analysis of the Classification Performance of Machine Learning Classifiers and Deep Neural Network Classifier for Prediction of Parkinson Disease

The invention discloses an automatic text abstraction method, and the method comprises the steps: enabling a CNN (Convolutional Neural Network) and a self-attention mechanism self-to carry out the self-attention of the CNN; combining an action, an information selection gate and a Maxout network for use, and controlling inflow of original text information in the information coding stage so as to select important information; and meanwhile, further selecting the most important decoding information as output by using the Maxout network in the decoding stage. According to the method, the problem of repeated abstract word generation is effectively solved, and useless information can be filtered out as much as possible on the basis that important information of an original text is reserved.

Automatic text abstraction method

The invention discloses an image encryption method based on chaotic tent mapping and DNA decoding, comprising the following steps: S1, acquiring ASCII sequence of common image, tent map and parametersthereof, and obtaining dimensions of common image; S2, generating a key image and an initial condition value of that chaotic image by using the chaotic tent map; S3, obtaining a new chaotic image condition value by using a logical map; S4, rounding down to obtain a constant value; 5, selecting DNA coding rule; S6, obtaining a DNA sequence of a common image; S7, obtaining the DNA sequence of the key image; 8, selecting DNA algebra operation; S9, obtaining DNA sequence of the semi-cipher image by DNA algebra operation; 10, selecting DNA decoding rule; 11, DNA decoding according to that DNA decode rule to obtain a cipher image; The invention solves the problems of low security and practicability in the prior art and direct generation of a key.

An image encryption method based on chaotic tent mapping and DNA decoding

The invention discloses a switching method from a 2D (Two Dimensional) image to a 3D (Three Dimensional) image. The invention provides a novel rapid algorithm for converting 2D content into 3D content; the novel rapid algorithm is novel and rapid; the time complexity is reduced and the complexity of a memory is reduced; the calculation cost is reduced and a high-definition image/video is more vivid; the quality of a depth image is improved and the timeliness of 3D output is improved.

Sun Ruinan

Papers

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

Comparative Analysis of the Classification Performance of Machine Learning Classifiers and Deep Neural Network Classifier for Prediction of Parkinson Disease

Automatic text abstraction method

An image encryption method based on chaotic tent mapping and DNA decoding

Switching method from 2D (Two Dimensional) image to 3D (Three Dimensional) image