Accuracy Of Classifier Research Articles

Классификация сигналов ЭКГ на основе цифровой обработки сигналов, технического выбора функций и радиочастотного классификатора

Determining the optimal integration between features and classifiers has a significant effect on the performance of automatic heartbeat diagnostic systems. This importance stands out when dealing with critical applications that contain limited resources devices and require accurate and fast heartbeat classifiers to help the doctor make an accurate and quick diagnosis of heart diseases. Aiming at this task, this paper introduces a novel approach for choosing the optimal features of the ECG signal to be used with the Random Forest (RF) classifier following the inter-patient method for ECG signals division and obeying the instructions of the "Association for the Advancement of Medical Instrumentation (AAMI)." The features were chosen based on the concept of "Mutual Information Ranking (MIR)." The presented framework is comprehensive in terms of performing all the necessary processes efficiently, starting from ECG digital signal processing, segmentation, feature extraction, feature selection, and ending with ECG classification. The results of the experiments demonstrate that features corresponding to the normalized QRS width and the normalized RR intervals are the most influential features in the heartbeat classification. All tests were conducted using real ECG signals taken from the "MIT-BIH" Arrhythmia Database (MIT-BIH-ARR-DB). The suggested scheme attained the following F1-scores: 91.02%, 73.17%, and 98.04% in the classification of the Ventricular Ectopic Beats (V or VEB), Supraventricular Ectopic Beats (S or SVEB), and Normal Beats (N or NB), respectively. The overall accuracy was 96.26%. Despite its relative simplicity and reliance on few features, the proposed approach outperforms most of the reported state-of-the-art.

Clustering-based incremental learning for imbalanced data classification

Imbalanced data classification presents a significant challenge when there is a substantial disparity in sample sizes across different classes. This issue severely affects classifier accuracy in predicting minority classes, hampering numerous real-world applications. Traditional methods address data imbalance by using undersampling or oversampling techniques. However, these methods may lead to information loss during sample reduction or introduce noise and model bias through synthetic sample generation. In this paper, we introduce DRIL, an innovative clustering-based incremental learning approach designed to overcome these limitations and improve the classification of minority class samples. Specifically, we employ a “two-step clustering” method to rebalance the dataset, partitioning it into similar and representative sub-dataset. Subsequently, incremental learning is applied to enable the classifier to gradually acquire knowledge about these sub-data, establishing a comprehensive understanding of all features present in the imbalanced dataset. Experimental results on twenty datasets demonstrate that our incremental learning-based algorithm outperforms baseline methods in correctly classifying minority classes while exhibiting improved precision and F1 score performance.

Classification and automatic scoring of arousal intensity during sleep stages using machine learning

Arousal during sleep can result in sleep fragmentation and various physiological effects, impairing cognitive function and raising blood pressure and heart rate. However, the current definition of arousal has limitations in assessing both amplitude and duration, making it challenging to measure sleep fragmentation accurately. Moreover, there is inconsistency among inter-raters in arousal scoring, which renders it susceptible to subjective variability. Therefore, this study aims to identify a highly accurate classifier for each sleep stage by employing optimized feature selection and machine learning models. According to electroencephalography (EEG) signals during the arousal phase, the intensity level was categorized into four levels. For control, the non-arousal cases were used as level 0 and referred as sham arousal, resulting in five arousal intensity levels. Wavelet transform was applied to analyze sleep arousal to extract features from EEG. Based on these features, we classified arousal intensity levels through machine learning algorithms. Due to the different characteristics of EEG in each sleep stage, the classification model was optimized for the four sleep stages. Excluding sham arousals, a total of 13,532 arousal events were used. The lowest intensity in the entire data, level 1, was computed to be 3107, level 2 was 3384, level 3 was 3472, and the highest intensity of level 4 was 3,569. The optimized classification model for each sleep stage achieved an average sensitivity of 82.68%, specificity of 95.68%, and AUROC of 96.30%. The sensitivity of the control, arousal intensity level 0, was 83.07%, a 1.25% increase over the unoptimized model and a 14.22% increase over previous research. This study used machine learning techniques to develop classifiers for each sleep stage, improving the accuracy of arousal intensity classification. The classifiers showed high sensitivity and specificity and revealed the unique characteristics of arousal intensity during different sleep stages. These findings represent a novel approach to arousal research and have implications for developing more accurate predictive models in sleep research.

Pattern recognition for EMG based forearm orientation and contraction in myoelectric prosthetic hand

Communication is an essential component of human nature. It connects humans, allowing them to learn, grow, col-laborate, and resolve conflicts. Several aspects of human society, relationships, and growth would be significantly hampered in the absence of efficient communication. Hand gesture recognition is a way to interact with technology that can be particularly useful for individuals with disabilities. This hand gesture recognition is mainly employed in sign language translation, healthcare, rehabilitation, prosthesis, and Human-Computer Interaction (HCI). The high degree of dexterity is a main challenge for prosthetic limbs. In order to meet this challenge, hand gesture recognition is employed for the prosthetic limb, which can be used for rehabilitation. The objective of this article is to show the methodology for the recognition of hand gestures using Electromyography (EMG) signals. This article uses the pro-posed time domain feature extraction method called Absolute Fluctuation Analysis (AFA) along with the Root Mean Square (RMS) for the feature extraction method. Along with these feature extraction methods, repeated stratified K-fold cross validation is used for the validation of the classifiers such as the XGB classifier, the K-Nearest Neighbour (KNN) classifier, the Decision Tree classifier, the Random Forest classifier, and the SVM classifier, whose mean recognition accuracy is given by 93.26%, 87.42%, 85.26%, 92.23%, and 91.78%, respectively. The recognition accuracy of machine learning classifiers is being compared with state-of-the-art networks such as artificial neural net-works (ANN), long short-term memory (LSTM), bidirectional LSTM, gated recurrent units (GRU), and convolution-al neural networks (CNN), which provide recognition accuracy of 96.65%, 99.16%, 99.94%, and 99.99%, respectively.

AutoEval: Are Labels Always Necessary for Classifier Accuracy Evaluation?

Understanding model decision under novel test scenarios is central to the community. A common practice is evaluating models on labeled test sets. However, many real-world scenarios see unlabeled test data, rendering the common supervised evaluation protocols infeasible. In this paper, we investigate such an important but under-explored problem, named Automatic model Evaluation (AutoEval). Specifically, given a trained classifier, we aim to estimate its accuracy on various unlabeled test datasets. We construct a meta-dataset: a dataset comprised of datasets (sample sets) created from original images via various transformations such as rotation and background substitution. Correlation studies on the meta-dataset show that classifier accuracy exhibits a strong negative linear relationship with distribution shift (Pearson's Correlation ). This new finding inspires us to formulate AutoEval as a dataset-level regression problem. Specifically, we learn regression models (e.g., a regression neural network) to estimate classifier accuracy from overall feature statistics of a test set. In the experiment, we show that the meta-dataset contains sufficient and diverse sample sets, allowing us to train robust regression models and report reasonable and promising predictions of the classifier accuracy on various test sets. We also provide insights into application scopes, limitations, and potential future directions of AutoEval.

Fruit recognition and classification based on tactile information of flexible hand

In the field of agricultural production, the sorting of agricultural products is a basic operation performed by robot manipulators. Agricultural products are fragile and easily damaged, and their shape, texture and size are different. In order to ensure grasping compliance and the integrity of agricultural products. In this study, firstly, a three-layer flexible tactile sensor was designed and fabricated. The performance of the sensor was tested. Three functions were used to fit the relationship between the sensor pressure and the output voltage, and the function with the best fitting degree was selected for sensor calibration. The sensor exhibits two kinds of sensitivity under the loading of 0 ∼ 4 N.When the applied pressure is 0 ∼ 2.75 N, the sensitivity is 1.21 V / N. When the applied pressure is 2.75 ∼ 4 N, the sensitivity is 0.24 V / N. Then, the sensor and the flexible gripper are combined to build a tactile information acquisition platform to collect the original data set of the tactile sequence generated when the flexible hand grabs the fruit, and the original data is preprocessed. Finally, we evaluated the performance of Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Naive Bayes (NB) and Neural Network (NN) in fruit classification. The results show that the RF classifier achieves the highest accuracy in the training and testing stages, which are 98.64% and 93.06%, respectively, and the accuracy of other classifiers ranges from 66.43% to 95.83%. Our work is helpful to the realization of robotic tactile perception and non-invasive grasping in the field of agriculture, and can be helpful to fruit picking, handling, sorting and other related fields.

Surface and Underwater Acoustic Source Recognition Using Array Feature Extraction Based on Machine Learning

Sound source recognition is an important application of passive sonar. How to distinguish between surface and underwater sound sources is always a difficult problem. In order to solve the problem of S/U sound source identification, this paper proposes a machine learning method based on array feature extraction, which has certain innovative value. Firstly, according to the experimental environment of SACLANT 1993, simulation data is generated based on KRAKEN. Secondly, the simulation data and experimental data are used to extract the array features. Thirdly, the accuracy, recall rate, F1 and accuracy of GBDT classifier in three different frequency bands are evaluated. The results show that the training model established by using the array feature extraction method can effectively solve the problem of poor accuracy of some channels in the single channel classification, and obtain good experimental accuracy. Finally, the experimental accuracy of the three bandwidths is 0.9920, 0.9857 and 0.9713, respectively.

Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults

Both wear-induced bearing failure and misalignment of the powertrain between the rotor and the electrical generator are common failure modes in wind-turbine motors. In this study, Semi-Supervised Learning (SSL) is applied to a fault detection and diagnosis solution. Firstly, a dataset is generated containing both normal operating patterns and seven different failure classes of the two aforementioned failure modes that vary in intensity. Several datasets are then generated, maintaining different numbers of labeled instances and unlabeling the others, in order to evaluate the number of labeled instances needed for the desired accuracy level. Subsequently, different types of SSL algorithms and combinations of algorithms are trained and then evaluated with the test data. The results showed that an SSL approach could improve the accuracy of trained classifiers when a small number of labeled instances were used together with many unlabeled instances to train a Co-Training algorithm or combinations of such algorithms. When a few labeled instances (fewer than 10% or 327 instances, in this case) were used together with unlabeled instances, the SSL algorithms outperformed the result obtained with the Supervised Learning (SL) techniques used as a benchmark. When the number of labeled instances was sufficient, the SL algorithm (using only labeled instances) performed better than the SSL algorithms (accuracy levels of 87.04% vs. 86.45%, when labeling 10% of instances). A competitive accuracy of 97.73% was achieved with the SL algorithm processing a subset of 40% of the labeled instances.Graphical abstractSteps and processes for approaching semi-supervised FDD of wind-turbine gearbox misalignment and imbalance faults

Negation Detection Techniques in Sentiment Analysis: A Survey

Negation is a linguistic phenomenon that can cause sentences to have their meanings reversed. It frequently inverts affirmative sentences into negative ones, affecting the polarity; therefore, the sentiment of the text also changes accordingly. Negation can be expressed differently, making it somewhat challenging to detect. As a result, detecting negation is critical for Sentiment Analysis (SA) system development and improvement and will increase classifier accuracy, but it also poses a significant conceptual and technical challenge. This paper aims to survey and gather the most recent research related to detecting negation in SA. Many researchers have worked and performed methods, including algorithmic, machine, and deep learning approaches such as Decision Tree (DT), Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naive Bayesian (NB), Logistic Regression (LR), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Bidirectional Long Short-Term Memory (BiLSTM), and other hybrid methods such as rule-based and machine learning, lexicon and machine learning, machine learning, and deep learning. It addresses and tries to identify the gaps in the current studies, laying the foundation for future studies in this field.

Uncertainty Quantification of Machine Learning Model Performance via Anomaly-Based Dataset Dissimilarity Measures

The use of Machine Learning (ML) models as predictive tools has increased dramatically in recent years. However, data-driven systems (such as ML models) exhibit a degree of uncertainty in their predictions. In other words, they could produce unexpectedly erroneous predictions if the uncertainty stemming from the data, choice of model and model parameters is not taken into account. In this paper, we introduce a novel method for quantifying the uncertainty of the performance levels attained by ML classifiers. In particular, we investigate and characterize the uncertainty of model accuracy when classifying out-of-distribution data that are statistically dissimilar from the data employed during training. A main element of this novel Uncertainty Quantification (UQ) method is a measure of the dissimilarity between two datasets. We introduce an innovative family of data dissimilarity measures based on anomaly detection algorithms, namely the Anomaly-based Dataset Dissimilarity (ADD) measures. These dissimilarity measures process feature representations that are derived from the activation values of neural networks when supplied with dataset items. The proposed UQ method for classification performance employs these dissimilarity measures to estimate the classifier accuracy for unseen, out-of-distribution datasets, and to give an uncertainty band for those estimates. A numerical analysis of the efficacy of the UQ method is conducted using standard Artificial Neural Network (ANN) classifiers and public domain datasets. The results obtained generally demonstrate that the amplitude of the uncertainty band associated with the estimated accuracy values tends to increase as the data dissimilarity measure increases. Overall, this research contributes to the verification and run-time performance prediction of systems composed of ML-based elements.

Deep learning approach to improve the recognition of hand gesture with multi force variation using electromyography signal from amputees

Variations in muscular contraction are known to significantly impact the quality of the generated EMG signal and the output decision of a proposed classifier. This is an issue when the classifier is further implemented in prosthetic hand design. Therefore, this study aims to develop a deep learning classifier to improve the classification of hand motion gestures and investigate the effect of force variations on their accuracy on amputees. The contribution of this study showed that the resulting deep learning architecture based on DNN (deep neural network) could recognize the six gestures and robust against different force levels (18 combinations). Additionally, this study recommended several channels that most contribute to the classifier's accuracy. Also, the selected time domain features were used for a classifier to recognize 18 combinations of EMG signal patterns (6 gestures and three forces). The average accuracy of the proposed method (DNN) was also observed at 92.0 ± 6.1 %. Moreover, several other classifiers were used as comparisons, such as support vector machine (SVM), decision tree (DT), K-nearest neighbors, and Linear Discriminant Analysis (LDA). The increase in the mean accuracy of the proposed method compared to other conventional classifiers (SVM, DT, KNN, and LDA), was 17.86 %. Also, the study's implication stated that the proposed method should be applied to developing prosthetic hands for amputees that recognize multi-force gestures.

Satellite-based ensemble intelligent approach for predicting forest fire: a case of the Hyrcanian forest in Iran.

A machine learning-based approach is applied to simulate and forecast forest fires in the Golestan province in Iran. A dataset for no-fire, medium confidence (MC) fire events, and high confidence (HC) fire events is constructed from MODIS-MOD14A2. Nine climate variables from NASA's FLDAS are used as input variables, and 12 dates and 915 study points are considered. Three machine learning ensemble multi-label classifiers, gradient boosting (GBC), random forest (RFC), and extremely randomized tree (ETC), are used for forest fire simulation for the period 2000 to 2021, and ETC is found to be the most accurate classifier. Future fire projection for the near-future period of 2030 to 2050 is carried out with the ETC model, using CMIP6 EC-Earth3-SSP245 General Circulation Model (GCM) data. It is projected that MC forest fire occurrences will decrease, while HC forest fire occurrences will increase, and that the summer months, especially September, will be the most affected by fire.

An Automated Prostate-cancer Prediction System (APPS) Based on Advanced DFO-ConGA2L Model using MRI Imaging Technique

The prostate cancer is a deadly form of cancer that assassinates a significant number of men due of its mediocre identification process. Images from people with cancer include important and intricate details that are difficult for conventional diagnostic methods to extract. This work establishes a novel Automated Prostate-cancer Prediction System (APPS) model for the goal of detecting and classifying prostate cancer utilizing MRI imaging sequences. The supplied medical image is normalized using a Coherence Diffusion Filtering (CDFilter) approach for improved quality and contrast. The appropriate properties are also extracted from the normalized image using the morphological and texture feature extraction approach, which helps to increase the classifier's accuracy. In order to train the classifier, the most important properties are also selected utilizing the cutting-edge Dragon Fly Optimized Feature Selection (DFO-FS) algorithm. Using this method greatly improves the classifier's overall disease diagnosis performance in less time and with faster processing. More specifically, the provided MRI input data are used to categorize the prostate cancer-affected and healthy tissues using the new Convoluted Gated Axial Attention Learning Model (ConGA2L) based on the selected features. This study compares and validates the performance of the APPS model by looking at several aspects using publicly available prostate cancer data.

An overlapping minimization-based over-sampling algorithm for binary imbalanced classification

Imbalanced learning is an important branch of machine learning. It addresses the challenge of improving classifier accuracy for minority classes in imbalanced data sets. Currently, the mainstream methods for handling imbalanced learning are the synthetic minority oversampling technique (SMOTE) and its variants, which generate synthetic minority class samples to balance the dataset. However, existing methods suffer from issues such as increased sample overlap, exacerbated intra-class imbalance, and are sensitive to parameter settings. These issues make it challenging to generate high-quality minority class samples and can adversely affect the dataset. To address these challenges, this study proposes a novel overlapping minimization-based over-sampling (OMOS) algorithm for binary imbalanced classification. The OMOS algorithm consists of four steps: clustering, filtering, auto-encoding, and oversampling. In the clustering step, the mean shift algorithm is utilized to cluster the original dataset and identify clusters that belong to the minority class. In the filtering step, safe samples are selected that maintain consistent labels before and after clustering. Then, in the auto-encoding step, autoencoders are utilized to capture the distribution characteristics of safe samples within each minority class cluster. Finally, in the last step, minority class samples are generated based on the probability distribution learned from safe samples. Furthermore, OMOS introduces a novel approach to compute suitable sampling rates for each minority class cluster to handle intra-class imbalance. Experimental results show that the proposed OMOS algorithm outperforms six state-of-the-art SMOTE-based oversampling algorithms on 20 real-world imbalanced datasets and four classifiers: naive Bayes classifier, support vector machine, logistic regression, and decision trees. This demonstrates that OMOS is effective for binary imbalanced classification tasks.

Bi-stage feature selection for crop mapping using grey wolf metaheuristic optimization

Machine learning classifiers have been widely used for crop mapping; however, employing numerous features can reduce classifier accuracy. Feature selection is a widely recognized technique that addresses the issue of high dimensionality by choosing the most relevant subset of features with the highest degree of relevance and practicality. Therefore, the optimal combination of feature selection methods and classifiers for high-precision crop mapping remains an open problem. In this study, we introduce a novel hybrid feature selection approach to obtain the optimal subset of features for effective crop mapping. Initially, spectro-temporal remote sensing features from the dataset were ranked using two distinct feature selection techniques: Mutual Information (MI) and ReliefF filter. The most relevant features from each filter-based approach were combined into a union subset. Subsequently, the Gray Wolf Optimization (GWO), a metaheuristic optimization technique, was applied to enhance the feature set generated in the initial step. In the final stage, a random forest classifier leveraged the optimized feature subset for accurate crop type prediction. The effectiveness of the proposed approach was evaluated in Behiera governorate, Egypt. Comparative analysis against existing crop mapping methods demonstrated the superior performance of the proposed approach, achieving an accuracy rate of 82%. Furthermore, this approach showcased a significant reduction in the feature count, streamlining the set from 308 down to only 26 features.

Feature decoupling and interaction network for defending against adversarial examples

Recently, it was found that deep neural networks (DNNs) are susceptible to adversarial input perturbations. Most defense strategies adopt the denoising method based on preprocessing, which mitigates the impacts of adversarial perturbations on DNNs by learning the distributions of nonadversarial datasets and projecting adversarial inputs into the learned nonadversarial manifolds. However, existing defense strategies commonly focus on reconstructing clean images while ignoring the role of adversarial perturbations, which results in the reconstructed images failing to achieve the visual quality and classification accuracy of the original clean images, and the induced adversarial robustness improvement is limited. This paper proposes a feature decoupling-interaction network (FDIN), which introduces the concepts of clean features and adversarial features to separate the two kinds of features from the input adversarial examples (AEs) in a feature decoupling-interaction manner. The clean features are used to reconstruct the input image so that it is infinitely close to the original clean image, and the adversarial features are used to reconstruct the adversarial perturbations. Adversarial perturbations are removed from the adversarial examples across multiple cross cycles to improve further the reconstructed image's visual quality and classification accuracy. The features of the original clean image are used as prior knowledge to guide the network to learn the clean features of the adversarial examples and improve the classification accuracy of the model on the clean examples. In addition, a classification loss function based on the Carlini & Wagner (CW) attack algorithm is used instead of the conventional cross-entropy loss function to improve the adversarial robustness of the FDIN. The experimental results show that the proposed method achieves better defense performance than the current state-of-the-art methods on both standard tests and various attack tests and even exceeds the test accuracy of the target classifier on the original test set.

Assessing the diagnostic utility of the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system using real-world data

BackgroundGaucher disease (GD) is a rare autosomal recessive condition associated with clinical features such as splenomegaly, hepatomegaly, anemia, thrombocytopenia, and bone abnormalities. Three clinical forms of GD have been defined based on the absence (type 1, GD1) or presence (types 2 and 3) of neurological signs. Early diagnosis can reduce the likelihood of severe, often irreversible complications. The aim of this study was to validate the ability of factors from the Gaucher Earlier Diagnosis Consensus (GED-C) scoring system to discriminate between patients with GD1 and controls using real-world data from electronic patient medical records from Maccabi Healthcare Services, Israel’s second-largest state-mandated healthcare provider.MethodsWe applied the GED-C scoring system to 265 confirmed cases of GD and 3445 non-GD controls matched for year of birth, sex, and socioeconomic status identified from 1998 to 2022. The analyses were based on two databases: (1) all available data and (2) all data except free-text notes. Features from the GED-C scoring system applicable to GD1 were extracted for each individual. Patients and controls were compared for the proportion of the specific features and overall GED-C scores. Decision tree and random forest models were trained to identify the main features distinguishing GD from non-GD controls.ResultsThe GED-C scoring distinguished individuals with GD from controls using both databases. Decision tree models for the databases showed good accuracy (0.96 [95% CI 0.95–0.97] for Database 1; 0.95 [95% CI 0.94–0.96] for Database 2), high specificity (0.99 [95% CI 0.99–1]) for Database 1; 1.0 [95% CI 0.99–1] for Database 2), but relatively low sensitivity (0.53 [95% CI 0.46–0.59] for Database 1; 0.32 [95% CI 0.25–0.38]) for Database 2). The clinical features of splenomegaly, thrombocytopenia (< 50 × 109/L), and hyperferritinemia (300–1000 ng/mL) were found to be the three most accurate classifiers of GD in both databases.ConclusionIn this analysis of real-world patient data, certain individual features of the GED-C score discriminate more successfully between patients with GD and controls than the overall score. An enhanced diagnostic model may lead to earlier, reliable diagnoses of Gaucher disease, aiming to minimize the severe complications associated with this disease.

Oil Palm Leaves Phenotyping using Biomarkers Derived from Raman Spectra

The efficient production of oil from oil palm trees is heavily dependent on their health status, reflected in the oil extraction rate (OER). The 17th frond of the oil palm trees contains a significant amount of organic compounds that directly influence the overall health of the tree. Achieving an optimal balance of essential nutrients such as nitrogen (N), phosphorus (P), and potassium (K) is crucial for classifying a tree as healthy, as it results in an increased oil to bunch and fruit to bunch ratio. To accurately assess the health level of oil palm trees, this study explores the application of Raman spectroscopy, a non-invasive technique in determining the molecular fingerprint of an organic sample. In this research, Raman spectroscopy is employed to determine the health level of oil palm trees, and a machine learning-based health level classification algorithm is developed. The algorithm analyzes the organic compounds found in oil palm leaves, which were collected from 20 different trees. The extracted spectral features from these leaves are used to classify them into two health levels: healthy and not healthy. For this purpose, 31 machine learning models are tested to identify the most accurate classifier. The findings reveal that the Tree and fine K-Nearest Neighbors (KNN) classifier demonstrates the highest overall accuracy of 95% using three significant features, namely the Raman intensity, Full Width at Half Maximum (FWHM), and area under the curve. This result signifies the potential of Raman spectroscopy as a reliable and promising method for non-invasively phenotyping oil palm leaves, enabling precise prediction of the health status of oil palm trees.

Classification and Regression Based on Decision Tree Algorithm for Machine Learning

Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed. Keywords: Machine Learning, Supervised, Classification, Regression, Decision Tree.

Exploring Feature Selection and Machine Learning Algorithms for Predicting Diabetes Disease

One of the most common diseases in the world is the chronic diabetes. Diabetes has a direct impact on the lives of millions of people worldwide. Diabetes can be controlled and improved with early diagnosis, but the majority of patients continue to live with it. There is a dispirit need to a system to anticipate and select the people who are most likely to be diabetes in the future. Diagnosing the future diseased person without taking any blood or glucose screening tests, is the main goal of this study. This paper proposed a deep-learning model for diabetes disease prediction. The proposed model consists of three main phases, data pre-processing, feature selection and finally different classifiers. Initially, during the data pre-processing stage, missing values are handled, and data normalization is applied to the data. Then, three techniques are used to select the most important features which are mutual information, chi-squared and Pearson correlation. After that, multiple machine learning classifiers are used. Four experiments are then conducted to test our models. Additionally, the effectiveness of the proposed model is evaluated against that of other well-known machine learning techniques. The accuracy, AUC, sensitivity, and F-measure of the linear regression classifier are higher than those of the other methods, according to experimental data, which show that it performs better. The suggested model worked better than traditional methods and had a high accuracy rate for predicting diabetic disease.