University Of California , Irvine Research Articles

Improving The Accuracy Of The C4.5 Algorithm Using Particle Swarm Optimization To Predicate Diabetes Patients

Diabetes is a disease that occurs due to an increase in blood sugar levels. Increased sugar levels can trigger death because they can cause damage to blood vessels, nerves, and other internal structures. To avoid bad consequences, it is important to predict whether a person has diabetes or not based on the information that each patient has. The method that can be used to predict whether someone has diabetes or not is a classification method. The C4.5 algorithm is one of the classification algorithms that can be used to predict diabetics. Although the C4.5 algorithm can be used to predict, getting a high accuracy value is also an important measurement. One thing that can be done to increase the accuracy value is to select the attribute that most influences a person to have diabetes or not. Feature selection greatly affects the accuracy of the C4.5 algorithm, particle swarm optimization is used as a feature selection method in this study. The purpose of this study is to increase the value of accuracy to predict whether someone has diabetes or not through feature selection using particle swarm optimization. The dataset used is the Pima Indian Diabetes Databases (PIDD) from the University of California Irvine (UCI) Machine Learning Repository. The results of the study show that feature selection using particle swarm optimization before using the C4.5 algorithm can increase accuracy by 6% from the previous accuracy value of 75%. The selected features are 4 attributes out of 9 attributes. The four attributes are Glucose, SkinThickness, BMI, and DiabetesPedigreeFunction. The results of feature selection using particle swarm optimization can increase the accuracy of predictions of whether someone suffering from diabetes or not.

Neural Fuzzy Hybrid Rule-Based Inference System with Test Cases for Prediction of Heart Attack Probability

Heart disease has reached to the number one position in last decade in terms of mortality rate, and more wretchedly, heart attack has affected life in 80% of the cases. Cardiac arrest is an incurable incongruity that requires special treatment and cure. It has been a key research area for many years, and the number of researchers across the globe is devoted toward finding the optimal solution to avoid the ill-effect of this disease. Along with predicting heart disease, if focus moves towards prevention of heart attack as well, then this could result in major life saver area for masses. This research work is fully devoted toward finding out the probability of heart attack so that people can take preventive measure before it hit the wall. This research proposed the neural fuzzy inference system (NFIS) to represent the training data formed from the n-dimensions of functions. The NFIS consists of error computing module to improve the learning instructions when the errors have been measured, initially the membership functions are defined, and the parameters of membership functions are activated and learnt through when needed for an operation. The proposed methodology has experimented with sample test cases on Cleveland heart disease dataset from University of California Irvine (UCI) repository with the integration of supporting dependable and nondependable parameters, causing-factors, and data-matrices. This research has integration more than 13000 fuzzification rules to generate best decision-making, normalization process, planting techniques to create the feasibility to compute the heart attack probability and achieved 94 percentage of accuracy. This research can be extendable to build auto-altering and advise system with integration hardware peripheral circuit devices.

Novel production prediction model of gasoline production processes for energy saving and economic increasing based on AM-GRU integrating the UMAP algorithm

Gasoline, as an extremely important petroleum product, is of great significance to ensure people's living standards and maintain national energy security. In the actual gasoline industrial production environment, the point information collected by industrial devices usually has the characteristics of high dimension, high noise and time series because of the instability of manual operation and equipment operation. Therefore, it is difficult to use the traditional method to predict and optimize gasoline production. In this paper, a novel production prediction model using an attention mechanism (AM) based gated recurrent unit (GRU) (AM-GRU) integrating the uniform manifold approximation and projection (UMAP) is proposed. The data collected in the industrial plant are processed by the box plot to remove the data outside the quartile. Then, the UMAP is used to remove the strong correlation between the data, which can improve the running speed and the performance of the AM-GRU. Compared with the existing time series data prediction method, the superiority of the AM-GRU is verified based on University of California Irvine (UCI) benchmark datasets. Finally, the production prediction model of actual complex gasoline production processes for energy saving and economic increasing based on the proposed method is built. The experiment results show that compared with other time series data prediction models, the proposed model has better stability and higher accuracy with reaching 0.4171, 0.9969, 0.2538 and 0.5038 in terms of the mean squared error, the average absolute accuracy, the mean squared error and the root mean square error. Moreover, according to the optimal scheme of the raw material, the inefficiency production points can be expected to increase about 0.69 tons of the gasoline yield and between about $645.1 and $925.6 of economic benefits of industrial production.

Attribute Reduction Based on Lift and Random Sampling

As one of the key topics in the development of neighborhood rough set, attribute reduction has attracted extensive attentions because of its practicability and interpretability for dimension reduction or feature selection. Although the random sampling strategy has been introduced in attribute reduction to avoid overfitting, uncontrollable sampling may still affect the efficiency of search reduct. By utilizing inherent characteristics of each label, Multi-label learning with Label specIfic FeaTures (Lift) algorithm can improve the performance of mathematical modeling. Therefore, here, it is attempted to use Lift algorithm to guide the sampling for reduce the uncontrollability of sampling. In this paper, an attribute reduction algorithm based on Lift and random sampling called ARLRS is proposed, which aims to improve the efficiency of searching reduct. Firstly, Lift algorithm is used to choose the samples from the dataset as the members of the first group, then the reduct of the first group is calculated. Secondly, random sampling strategy is used to divide the rest of samples into groups which have symmetry structure. Finally, the reducts are calculated group-by-group, which is guided by the maintenance of the reducts’ classification performance. Comparing with other 5 attribute reduction strategies based on rough set theory over 17 University of California Irvine (UCI) datasets, experimental results show that: (1) ARLRS algorithm can significantly reduce the time consumption of searching reduct; (2) the reduct derived from ARLRS algorithm can provide satisfying performance in classification tasks.

Prediction of patient survival from heart failure using a cox-based model

The existing heart failure risk prediction models are developed based on machine learning predictors. The objective of this study is to identify the key risk factors that affect the survival time of heart patients and to develop a heart failure survival prediction model using the identified risk factors. A cox proportional hazard regression method is applied to generate the proposed heart failure survival model. We used the dataset from the University of California Irvine (UCI) clinical heart failure data repository. To develop the model we have used multiple risk factors such as age, anemia, creatinine phosphokinase, diabetes history, ejection fraction, presence of high blood pressure, platelet count, serum creatinine, sex, and smoking history. Among the risk factors, high blood pressure is identified as one of the novel risk factors for heart failure. We have validated the performance of the model via statistical and empirical validation. The experimental result shows that the proposed model achieved good discrimination and calibration ability with a C-index (receiver operating characteristic (ROC) of being 0.74 and a log-likelihood ratio of 81.95 using 11 degrees of freedom on the validation dataset.

An empirical study on machine learning algorithms for heart disease prediction

In recent years, machine learning is attaining higher precision and accuracy in clinical heart disease dataset classification. However, literature shows that the quality of heart disease feature used for the training model has a significant impact on the outcome of the predictive model. Thus, this study focuses on exploring the impact of the quality of heart disease features on the performance of the machine learning model on heart disease prediction by employing recursive feature elimination with cross-validation (RFECV). Furthermore, the study explores heart disease features with a significant effect on model output. The dataset for experimentation is obtained from the University of California Irvine (UCI) machine learning dataset. The experiment is implemented using a support vector machine (SVM), logistic regression (LR), decision tree (DT), and random forest (RF) are employed. The performance of the SVM, LR, DT, and RF models. The result appears to prove that the quality of the feature significantly affects the performance of the model. Overall, the experiment proves that RF outperforms as compared to other algorithms. In conclusion, the predictive accuracy of 99.7% is achieved with RF.

Improved K‐means algorithm for clustering non‐spherical data

AbstractAs one of the commonly used data mining algorithms, K‐means has the advantage of fast clustering speed, but the disadvantage is that it is less effective for clustering non‐spherical data. An improved K‐means algorithm (IK‐means) is proposed to enhance clustering efficiency for non‐spherical data. The original dataset is clustered into a relatively larger number of high‐density sub‐clusters, and the final result is obtained by merging connected sub‐clusters respectively. The connectivity among sub‐clusters is evaluated by the sub‐clusters density and the nearest distance class between sub‐clusters. By testing on University of California, Irvine(UCI) datasets and several other artificial simulation datasets, the comparison of proposed IK‐means algorithm against DBSCAN, KGFCM shows its clustering capability for data of arbitrary shape. The clustering Adjusted Rand Index (ARI) value for 72,000 sizes data is 24% higher than DBSCAN, and 95.2% higher than KGFCM. For larger datasets, the IK‐means algorithm is faster than DBSCAN and KGFCM.

A hybrid cost-sensitive machine learning approach for the classification of intelligent disease diagnosis

Automatic risk classification of diseases is one of the most significant health problems in medical and healthcare domain. However, the related studies are relative scarce. In this paper, we design an intelligent diagnosis model based on optimal machine learning algorithms with rich clinical data. First, the disease risk classification problem based on machine learning is defined. Then, the K-means clustering algorithm is used to validate the class label of given data, thereby removing misclassified instances from the original dataset. Furthermore, naive Bayesian algorithm is applied to build the final classifier by using 10-fold cross-validation method. In addition, a novel class-specific attribute weighted approach is adopted to alleviate the conditional independence assumption of naive Bayes, which means we assign each disease attribute a specific weight for each class. Last but not least, a hybrid cost-sensitive disease risk classification model is formulated, and a practical example from the University of California Irvine (UCI) machine learning database is used to illustrate the potential of the proposed method. Experimental results demonstrate that the approach is competitive with the state-of-the-art classifiers.

Detecting chronic kidney disease by using a voting classifier


 
 
 Chronic Kidney Disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. Since there are no obvious symptoms during the early stage of CKD, patients often fail to notice the disease. Early detection of CKD enables patients to receive timely treatment to ameliorate the progression of this disease. Machine learning models can effectively aid clinicians achieve this goal due to their fast and accurate recognition performance. In this, we proposed a machine learning methodology for diagnosing CKD. The CKD data set was obtained from the University of California Irvine (UCI) machine learning repository, which has a large number of missing values. In general we have six machine learning algorithms like logistic regression, random forest, support vector machine, k-nearest neighbour, naive Bayes, classifier and feed forward neural network to establish models. In existing system random forest achieved the performance with 99.75% diagnosis accuracy. In proposed system, By analysing the misjudgments generated by the above model, we proposed an integrated model that combines logistic regression and random forest by using perceptron which could achieve an efficient accuracy of 99.83% after ten times of simulation. Hence, we speculated that this methodology could be applicable to more complicated clinical data for disease diagnosis.
 
 

Commodity demand forecasting using modulated rank reduction for humanitarian logistics planning

Demand prediction for humanitarian logistics is a complex problem with immediate real-world consequences. This paper examines fuel demand during two regional humanitarian crisis events and the supply chain operated by the US Government as part of Operation Unified Response. Because typical machine learning algorithms require large amounts of training data, our methods for predictive analysis depend on rapid training of a model where re-sampling would not be useful due to dynamic time-series data. We propose an online robust principal components analysis (RPCA) model combined with a long short-term memory (LSTM) recurrent network to address this challenge. Our computational results demonstrate that the proposed model can predict demand efficiently on real-world humanitarian supply datasets and well-known benchmark datasets in the University of California, Irvine (UCI) Machine Learning Repository. This method also allows us to tune training lag in online learning.

RETRACTED: Detection and classification of epilepsy using hybrid convolutional neural network

In recent years, more than 50 million people have been affected by the epilepsy, neurological disorder diseases. To monitor the situation of the epilepsy patient requires experienced and skilled person. In order to overcome these issues, autonomous detection of electroencephalogram (EEG) signal by deep learning model has evolved. Convolutional neural network (CNN) is one of the sub-category of neural network and widely used in the various field such as weather forecasting, signal processing and medical applications. In this article, the University of California Irvine (UCI) respiratory EEG signals are used to analyse the proposed hybrid CNN and results are compared to the pre-trained GoogleNet Network. EEG signals are initially converted into three different forms such as scalogram, spectrogram and time domain images and classification of images are carried out by the pre-trained GoogleNet network results in an accuracy of 85%. Then time domain images are combined with spectrogram and scalogram EEG signal separately and detection has been carried out by the CNN. It is found that the CNN network yields an accuracy of 92% which was higher than the pre-trained GoogleNet. To enhance the classification accuracy further, scalogram, spectrogram and time domain images are combined as single input images and applied to the CNN network and it results with the accuracy of 98%. The performance metrics such as Sensitivity, Specificity, F1 Score, Precision and misclassification rate of GoogleNet and proposed hybrid CNN networks are evaluated. It is observed from the result that proposed CNN results less than 10% misclassification rate, whereas for GoogleNet it was more than 20%. Similarly, the precision value of GoogleNet and proposed CNN networks are 82% and 93%, respectively.

Development of a graphical user interface software for the prediction of chronic kidney disease

Chronic Kidney Disease (CKD) is a severe kidney damage that is difficult to diagnose at the early stages due to the absence of clear symptoms. Late diagnosis of CKD is a common problem in low-income countries and is often associated with lower chances of survival. This study was designed to develop a user-friendly web-based graphical user interface (GUI) software for the prediction of CKD using artificial neural networks (ANNs). The model was developed using Python programming language and trained with 1200 instances of CKD datasets obtained from the University of California Irvine (UCI) machine learning repository. This dataset was split into 80% for training and 20% for testing achieved through an iterative process. A GUI software was developed based on the model using Django, an open-source python web development framework. The model achieved an accuracy of 95.83%, a precision of 100%, a specificity of 100%, and a sensitivity of 89.80%. The GUI software was effectively used to predict CKD and could be of immense benefit as a point of care application for early CKD prediction

Email classification analysis using machine learning techniques

PurposeIn this digital era, email is the most pervasive form of communication between people. Many users become a victim of spam emails and their data have been exposed.Design/methodology/approachResearchers contribute to solving this problem by a focus on advanced machine learning algorithms and improved models for detecting spam emails but there is still a gap in features. To achieve good results, features also play an important role. To evaluate the performance of applied classifiers, 10-fold cross-validation is used.FindingsThe results approve that the spam emails are correctly classified with the accuracy of 98.00% for the Support Vector Machine and 98.06% for the Artificial Neural Network as compared to other applied machine learning classifiers.Originality/valueIn this paper, Point-Biserial correlation is applied to each feature concerning the class label of the University of California Irvine (UCI) spambase email dataset to select the best features. Extensive experiments are conducted on selected features by training the different classifiers.

An Ensemble Clustering Framework Based on Hierarchical Clustering Ensemble Selection and Clusters Clustering

Ensemble clustering combines the results of multiple individual clustering methods for better results. Basically, all available clustering methods can be combined to produce final clusters. However, selecting a subset of optimal methods can reduce the complexity and increase the efficiency of ensemble clustering methods. This article examines the problem of selecting individual clustering methods to produce an ensemble hierarchical clustering method. Hierarchical clustering is a technique for grouping data at different scales by creating dendrograms. The aim is to select a subset of individual hierarchical clustering methods considering diversity and quality that can create an ensemble clustering method with minimal complexity. The proposed method consists of three main phases. The selection of a subset of individual hierarchical clustering methods is done in the first phase. In the second phase, the results of the selected clusters are re-clustered to create super-clusters. Super-clusters can combine clustering knowledge of different methods into one clustering form. Finally, the final clusters are formed by assigning each sample to a super-cluster with the shortest distance in the third phase. Experimental results on several datasets from the University of California Irvine (UCI) repository show that the proposed method performs better than the state-of-the-art algorithms.

Cuff-Less Blood Pressure Estimation From Photoplethysmography via Visibility Graph and Transfer Learning.

This paper presents a new solution that enables the use of transfer learning for cuff-less blood pressure (BP) monitoring via short duration of photoplethysmogram (PPG). The proposed method estimates BP with low computational budget by 1) creating images from segments of PPG via visibility graph (VG), hence, preserving the temporal information of the PPG waveform, 2) using pre-trained deep convolutional neural network (CNN) to extract feature vectors from VG images, and 3) solving for the weights and bias between the feature vectors and the reference BPs with ridge regression. Using the University of California Irvine (UCI) database consisting of 348 records, the proposed method achieves a best error performance of 0.00±8.46 mmHg for systolic blood pressure (SBP), and -0.04±5.36 mmHg for diastolic blood pressure (DBP), respectively, in terms of the mean error (ME) and the standard deviation (SD) of error, ranking grade B for SBP and grade A for DBP under the British Hypertension Society (BHS) protocol. Our novel data-driven method offers a computationally-efficient end-to-end solution for rapid and user-friendly cuff-less PPG-based BP estimation.

Adaptive multiple imputations of missing values using the class center

Big data has become a core technology to provide innovative solutions in many fields. However, the collected dataset for data analysis in various domains will contain missing values. Missing value imputation is the primary method for resolving problems involving incomplete datasets. Missing attribute values are replaced with values from a selected set of observed data using statistical or machine learning methods. Although machine learning techniques can generate reasonably accurate imputation results, they typically require longer imputation durations than statistical techniques. This study proposes the adaptive multiple imputations of missing values using the class center (AMICC) approach to produce effective imputation results efficiently. AMICC is based on the class center and defines a threshold from the weighted distances between the center and other observed data for the imputation step. Additionally, the distance can be an adaptive nearest neighborhood or the center to estimate the missing values. The experimental results are based on numerical, categorical, and mixed datasets from the University of California Irvine (UCI) Machine Learning Repository with introduced missing values rate from 10 to 50% in 27 datasets. The proposed AMICC approach outperforms the other missing value imputation methods with higher average accuracy at 81.48% which is higher than those of other methods about 9 – 14%. Furthermore, execution time is different from the Mean/Mode method, about seven seconds; moreover, it requires significantly less time for imputation than some machine learning approaches about 10 – 14 s.

An improved binary sparrow search algorithm for feature selection in data classification

Feature Selection (FS) is an important preprocessing step that is involved in machine learning and data mining tasks for preparing data (especially high-dimensional data) by eliminating irrelevant and redundant features, thus reducing the potential curse of dimensionality of a given large dataset. Consequently, FS is arguably a combinatorial NP-hard problem in which the computational time increases exponentially with an increase in problem complexity. To tackle such a problem type, meta-heuristic techniques have been opted by an increasing number of scholars. Herein, a novel meta-heuristic algorithm, called Sparrow Search Algorithm (SSA), is presented. The SSA still performs poorly on exploratory behavior and exploration-exploitation trade-off because it does not duly stimulate the search within feasible regions, and the exploitation process suffers noticeable stagnation. Therefore, we improve SSA by adopting: i) a strategy for Random Re-positioning of Roaming Agents (3RA); and ii) a novel Local Search Algorithm (LSA), which are algorithmically incorporated into the original SSA structure. To the FS problem, SSA is improved and cloned as a binary variant, namely, the improved Binary SSA (iBSSA), which would strive to select the optimal or near-optimal features from a given dataset while keeping the classification accuracy maximized. For binary conversion, the iBSSA was primarily validated against nine common S-shaped and V-shaped Transfer Functions (TFs), thus producing nine iBSSA variants. To verify the robustness of these variants, three well-known classification techniques, including k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest (RF) were adopted as fitness evaluators with the proposed iBSSA approach and many other competing algorithms, on 18 multifaceted, multi-scale benchmark datasets from the University of California Irvine (UCI) data repository. Then, the overall best-performing iBSSA variant for each of the three classifiers was compared with binary variants of 12 different well-known meta-heuristic algorithms, including the original SSA (BSSA), Artificial Bee Colony (BABC), Particle Swarm Optimization (BPSO), Bat Algorithm (BBA), Grey Wolf Optimization (BGWO), Whale Optimization Algorithm (BWOA), Grasshopper Optimization Algorithm (BGOA) SailFish Optimizer (BSFO), Harris Hawks Optimization (BHHO), Bird Swarm Algorithm (BBSA), Atom Search Optimization (BASO), and Henry Gas Solubility Optimization (BHGSO). Based on a Wilcoxon’s non-parametric statistical test (alpha =0.05), the superiority of iBSSA with the three classifiers was very evident against counterparts across the vast majority of the selected datasets, achieving a feature size reduction of up to 92% along with up to 100% classification accuracy on some of those datasets.

A novel approach for cardiovascular disease prediction using machine learning algorithms

AbstractFor the past few decades, cardiovascular disease has shown a binding impact on the country's mortality rate. The prediction of cardiovascular disease is more challenging during the process of clinical data analysis. The emergence of Machine Learning approaches paved the way to predict the disease and determining the consequences of the disease in the earlier stage to help the physicians during complex decision‐making. This work adopts k‐Nearest Neighbor as baseline classifier and ensemble X‐boost, Adaboost, and Random subspace classifier model to predict heart disease and predict the features of cardiovascular disease using Linear Support Vector Feature Measure (). This model considers the diverse combination of features to make the better classification process. The model shows superior performance with precision via Clinical Decision Support System. The factors that influence the cardiovascular disease need to predict, and better decision is taken during the critical condition. Here, the online available University of California Irvine (UCI) Machine Learning dataset is used for training and testing where 80% data is considered for training and 20% considered for testing purpose. The simulation is done in MATLAB 2020b simulation environment, and the outcomes are compared with various existing approaches. Here, performance metrics like accuracy, precision, F‐measure, stability rate, region of curve, and recall is measured to show the model efficiency. The prediction accuracy of the proposed model is 96% which is higher than existing approaches. The overall performance of proposed ensemble model is 96% accuracy, 97% precision, 95% sensitivity, 95% F‐measure, 93% Matthew's correlation coefficients, 4.53% False Positive Rate, 3.10% False Negative Rate, and 96% True Positive Rate, respectively.

Optimization-Based Ensemble Feature Selection Algorithm and Deep Learning Classifier for Parkinson's Disease.

PD (Parkinson's Disease) is a severe malady that is painful and incurable, affecting older human beings. Identifying PD early in a precise manner is critical for the lengthened survival of patients, where DMTs (data mining techniques) and MLTs (machine learning techniques) can be advantageous. Studies have examined DMTs for their accuracy using Parkinson's dataset and analyzing feature relevance. Recent studies have used FMBOAs for feature selections and relevance analyses, where the selection of features aims to find the optimal subset of features for classification tasks and combine the learning of FMBOAs. EFSs (ensemble feature selections) are viable solutions for combining the benefits of multiple algorithms while balancing their drawbacks. This work uses OBEFSs (optimization-based ensemble feature selections) to select appropriate features based on agreements. Ensembles have the ability to combine results from multiple feature selection approaches, including FMBOAs, LFCSAs (Lévy flight cuckoo search algorithms), and AFAs (adaptive firefly algorithms). These approaches select optimized feature subsets, resulting in three feature subsets, which are subsequently matched for correlations by ensembles. The optimum features are generated by OBEFSs the trained on FCBi-LSTMs (fuzzy convolution bi-directional long short-term memories) for classifications. This work's suggested model uses the UCI (University of California-Irvine) learning repository, and the methods are evaluated using LOPO-CVs (Leave-One-Person-Out-Cross Validations) in terms of accuracies, F-measure values, and MCCs (Matthews correlation coefficients).

Accurate electricity consumption prediction using enhanced long short‐term memory

In the present era, the exponential growth in the human population and technological advancements has dramatically increased the power demand. As electricity is being used at the same time as it is produced at the power plant, effective forecasting of energy usage is crucial for maintaining a reliable power supply. In this work, a novel deep learning model named enhanced long short-term memory (E-LSTM) is proposed to accurately predict electricity consumption in advance as this deep learning model will accurately predict the electricity consumption by adjusting the number of hidden layers and optimizing the hyper-parameters. Until attaining high accuracy, hyper-parameters like learning rate (LR) and epochs along with hidden layers are adjusted to analyze the relative performance of the model. Also, rectified linear activation function alleviates the vanishing gradient problem of the model. Also, this work evaluated the seven LR policies using crucial policy (CP) benchmark on keras framework. Experiments using various evaluation criteria demonstrate that the developed E-LSTM model is efficient. When comparing some of the other state-of-the-art forecasting techniques, it has the lowest values of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) over the UC Irvine (UCI) residential building dataset.