Machine Learning Techniques For Classification Research Articles

Unbinned multivariate observables for global SMEFT analyses from machine learning

Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset. Optimizing such interpretations requires the identification of observables that exhibit the highest possible sensitivity to the underlying theory parameters. In this work we develop a flexible open source frame-work, ML4EFT, enabling the integration of unbinned multivariate observables into global SMEFT fits. As compared to traditional measurements, such observables enhance the sensitivity to the theory parameters by preventing the information loss incurred when binning in a subset of final-state kinematic variables. Our strategy combines machine learning regression and classification techniques to parameterize high-dimensional likelihood ratios, using the Monte Carlo replica method to estimate and propagate methodological uncertainties. As a proof of concept we construct unbinned multivariate observables for top-quark pair and Higgs+Z production at the LHC, demonstrate their impact on the SMEFT parameter space as compared to binned measurements, and study the improved constraints associated to multivariate inputs. Since the number of neural networks to be trained scales quadratically with the number of parameters and can be fully parallelized, the ML4EFT framework is well-suited to construct unbinned multivariate observables which depend on up to tens of EFT coefficients, as required in global fits.

Emotion Detection in Code-Mixed Roman Urdu - English Text

Emotion detection is a widely studied topic in natural language processing due to its significance in a number of application areas. A plethora of studies have been conducted on emotion detection in European as well as Asian languages. However, a large majority of these studies have been conducted in monolingual settings, whereas little attention has been paid to emotion detection in code-mixed text. Specifically, merely one study has been conducted on emotion detection inRoman Urdu (RU)andEnglish (EN)code-mixed text despite the fact that such text is widely used in social media platforms. A careful examination of the existing study has revealed several issues which justify that this area requires attention of researchers. For instance, more than 37% of the messages in the contemporary corpus are monolingual sentences representing that a purely code-mixed emotion analysis corpus is non-existent. To that end, this study has scrapped 400,000 sentences from three social media platforms to identify 20,000 RU-EN code-mixed sentences. Subsequently, an iterative approach is employed to develop emotion detection guidelines. These guidelines have been used to develop a large RU-EN emotion detection (RU-EN-Emotion) corpus in which 20,000 sentences are annotated as Neutral or Emotion-sentence. The sentences having emotions are further annotated with the respective emotions. Subsequently, 102 experiments are performed to evaluate the effectiveness of six classical machine learning techniques and six deep learning techniques. The results show, (a) CNN is the most effective technique when used with GloVe embeddings, and (b) our developed RU-EN-Emotion corpus is more useful than the contemporary corpus, as it employs a two-level classification approach.

Machine Learning Classification Techniques Applied in modern-day Music Recommendation Systems

Music streaming platforms have increased in demand over recent years.Due to the covid-19 pandemic and lockdowns, there has been a substantial increasein users in music streaming platforms, where the music streaming market grew by $7.47 billion. Hence, the importance of recommendation systems in these platforms. The goal of this research paper is to address and provide insights into the behind the scenes to recommendation systems. To have the most accurate results, this paper will be based on pre-existing GTZAN audio samples as datasets for different classifiers and compare the results of accuracy and consistency of the model’s outcome for each genre. Confusion matricesare the finest way to determine results from each classifier. To identify limitations, advantages, and whether or not each classifier provides the most ideal outcomes that satisfy user demands. This process will include processes such as the sample input, feature extraction, and finally, put through classifiers.

Classification of the Residues after High and Low Order Explosions Using Machine Learning Techniques on Fourier Transform Infrared (FTIR) Spectra.

Forensic science is a field that requires precise and reliable methods for the detection and analysis of evidence. One such method is Fourier Transform Infrared (FTIR) spectroscopy, which provides high sensitivity and selectivity in the detection of samples. In this study, the use of FTIR spectroscopy and statistical multivariate analysis to identify high explosive (HE) materials (C-4, TNT, and PETN) in the residues after high- and low-order explosions is demonstrated. Additionally, a detailed description of the data pre-treatment process and the use of various machine learning classification techniques to achieve successful identification is also provided. The best results were obtained with the hybrid LDA-PCA technique, which was implemented using the R environment, a code-driven open-source platform that promotes reproducibility and transparency.

Sistem Pendukung Keputusan dalam Mendiagnosa Gejala TBC dengan Metode WASPAS dan CPI

Mycobacterium bacteria is the cause of tuberculosis, an infectious disease that attacks the respiratory tract in humans and can easily spread through the air. In 2012, WHO data showed that tuberculosis is an infectious disease that causes the second largest health problem in the world. Tuberculosis is a disease that can attack other parts of the body as well as the lungs. The sooner a person finds out he has tuberculosis and gets tested, the more likely he will recover faster. There are many detection methods, but many are time consuming. A decision support system is needed using the WASPAS method and the CPI method which are capable of diagnosing tuberculosis. To overcome this problem. By forming a decision tree represented by rules, this decision support system implements Rank Order Centroids, one of the classification techniques in machine learning used in the data mining process. This study produces a system that is expected to make it easier for the general public to obtain timely and accurate information for diagnosing tuberculosis. A decision support system for diagnosing tuberculosis was developed as a result of this study. After being tested with 100 patient data, 50 as training data and 50 as testing data, the Confusion Matrix produces an accuracy value of 90%.

Classifying Malignant and Benign Tumors of Breast Cancer

Breast cancer is the second major cause of cancer deaths in women. Machine learning classification techniques can be used to increase the precision of diagnosis and bring it closer to 100%, thus saving the lives of many people. This paper proposed four different models, built using different combinations of selected features and applying five ML classification techniques to all the models to identify the best model with the highest accuracy. It analyzes five machine learning techniques, namely logistic regression (LR), support vector machines (SVM), naive bayes (NB), decision trees (DT), and k-nearest neighbor (KNN), for prediction of breast cancer using the Wisconsin Diagnostic Breast Cancer Dataset on these four models. The objective of the paper is to find the best ML algorithm that can most accurately predict breast cancer for a particular model. The outcome of this paper helps the doctors to improvise the diagnosis by knowing the effect of combinations of symptoms with the growth of breast cancer.

Multi-objective evolutionary optimization for dimensionality reduction of texts represented by synsets.

Despite new developments in machine learning classification techniques, improving the accuracy of spam filtering is a difficult task due to linguistic phenomena that limit its effectiveness. In particular, we highlight polysemy, synonymy, the usage of hypernyms/hyponyms, and the presence of irrelevant/confusing words. These problems should be solved at the pre-processing stage to avoid using inconsistent information in the building of classification models. Previous studies have suggested that the use of synset-based representation strategies could be successfully used to solve synonymy and polysemy problems. Complementarily, it is possible to take advantage of hyponymy/hypernymy-based to implement dimensionality reduction strategies. These strategies could unify textual terms to model the intentions of the document without losing any information (e.g., bringing together the synsets "viagra", "ciallis", "levitra" and other representing similar drugs by using "virility drug" which is a hyponym for all of them). These feature reduction schemes are known as lossless strategies as the information is not removed but only generalised. However, in some types of text classification problems (such as spam filtering) it may not be worthwhile to keep all the information and let dimensionality reduction algorithms discard information that may be irrelevant or confusing. In this work, we are introducing the feature reduction as a multi-objective optimisation problem to be solved using a Multi-Objective Evolutionary Algorithm (MOEA). Our algorithm allows, with minor modifications, to implement lossless (using only semantic-based synset grouping), low-loss (discarding irrelevant information and using semantic-based synset grouping) or lossy (discarding only irrelevant information) strategies. The contribution of this study is two-fold: (i) to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using MOEA and (ii) to provide an experimental comparison of lossless and low-loss schemes for text representation. The results obtained support the usefulness of the low-loss method to improve the efficiency of classifiers.

Tsetlin Machine for Sentiment Analysis and Spam Review Detection in Chinese

In Natural Language Processing (NLP), deep-learning neural networks have superior performance but pose transparency and explainability barriers, due to their black box nature, and, thus, there is lack of trustworthiness. On the other hand, classical machine learning techniques are intuitive and easy to understand but often cannot perform satisfactorily. Fortunately, many research studies have recently indicated that the newly introduced model, Tsetlin Machine (TM), has reliable performance and, at the same time, enjoys human-level interpretability by nature, which is a promising approach to trade off effectiveness and interpretability. However, nearly all of the related works so far have concentrated on the English language, while research on other languages is relatively scarce. So, we propose a novel method, based on the TM model, in which the learning process is transparent and easily-understandable for Chinese NLP tasks. Our model can learn semantic information in the Chinese language by clauses. For evaluation, we conducted experiments in two domains, namely sentiment analysis and spam review detection. The experimental results showed thatm for both domains, our method could provide higher accuracy and a higher F1 score than complex, but non-transparent, deep-learning models, such as BERT and ERINE.

Explainable Convolutional Neural Networks: A Taxonomy, Review, and Future Directions

Convolutional neural networks (CNNs) have shown promising results and have outperformed classical machine learning techniques in tasks such as image classification and object recognition. Their human-brain like structure enabled them to learn sophisticated features while passing images through their layers. However, their lack of explainability led to the demand for interpretations to justify their predictions. Research on Explainable AI or XAI has gained momentum to provide knowledge and insights into neural networks. This study summarizes the literature to gain more understanding of explainability in CNNs (i.e., Explainable Convolutional Neural Networks). We classify models that made efforts to improve the CNNs interpretation. We present and discuss taxonomies for XAI models that modify CNN architecture, simplify CNN representations, analyze feature relevance, and visualize interpretations. We review various metrics used to evaluate XAI interpretations. In addition, we discuss the applications and tasks of XAI models. This focused and extensive survey develops a perspective on this area by addressing suggestions for overcoming XAI interpretation challenges, like models’ generalization, unifying evaluation criteria, building robust models, and providing interpretations with semantic descriptions. Our taxonomy can be a reference to motivate future research in interpreting neural networks.

An experimental study of tomato viral leaf diseases detection using machine learning classification techniques

Agriculture is the backbone of India and more than 50% of the population is dependent on it. With the increasing demand for food with the increase in population, it is the need of time that crops should be prevented against diseases. More than 1K acres of land with tomato diseases got affected in Pune only during this pandemic (2021). It could have been prevented by correct identification of the disease and then by corrective measures. This paper presents the experimental and comparative study of tomato leaf disease classification using various traditional machine learning algorithms like random forest (RF), support vector machines (SVM), naïve bayes (NB), and deep learning convolutional neural network (CNN) algorithm. In this study, it is perceived that CNN with a pre-trained Inception v3 model was able to detect and classify better than traditional methods with more than 95% accuracy.

Abstract TP97: Automated Assessment Of DWI-FLAIR Mismatch To Predict Stroke-onset Time

Background and Purpose: DWI-FLAIR mismatch is used to determine thrombolytic eligibility in patients with acute ischemic stroke (AIS) when time-to-stroke symptom (TSS) is unknown. Advances in artificial intelligence and machine learning (ML) techniques have shown promising results in automated assessment of TSS. We aimed to use the e-Stroke software (Brainomix, Oxford, UK) to automatically classify DWI-FLAIR mismatch in a cohort of patients with AIS and in a comparative analysis with an expert neuroradiologist. Methods: In this retrospective study, consecutive patients with AIS who had pretreatment MRI, and known TSS were included. DWI-FLAIR mismatch was evaluated by an expert neuroradiologist blinded to TSS. e-Stroke software, which uses a combination of statistical methods and ML classification techniques was used to automatically classify DWI-FLAIR status. TSS was dichotomized using a 4.5 hr cutoff to assess the diagnostic performance of the radiologist vs. e-Stroke software. Results: Thus far 113 patients are included, 53 female, age (mean ± SD): 68.9 ± 17. TSS was 371±249 min (mean ± SD) and the infarct volume was 16.4±26.5 ml (mean ± SD). A total of 60 patients had TSS > 4.5 hrs while 53 had TSS < 4.5 hrs. Diagnostic performance (sensitivity/specificity/accuracy in %) for determination of TSS using DWI-FLAIR mismatch was 75/58.5/66.7 for the radiologist and 43/85/64 for e-Stroke software; without statistical difference (AUC: 0.66 for radiologist, 0.64 for e-Stroke; Delong test, p= 0.59). Subgroup analysis of infarctions of at least 1mL or higher, improved the diagnostic performance for both radiologist (83.8/59.4/71.6) and e-Stroke software (66.7/75.0/71.0), without statistical difference (AUC: 0.71 for radiologist and 0.71 for e-Stroke; Delong test, p= 0.96). Conclusions: Results show that fully automated quantitative approach for DWI-FLAIR mismatch provides similar diagnostic accuracies to expert neuroradiologist in determination of TSS.

Abstract TP102: Automated Assessment Of Ischemic Core On Noncontrast Computed Tomography: A Comparative Analysis With CT Perfusion

Introduction: Application of machine learning (ML) algorithms has shown promising results in estimating ischemic core volumes using routine non-contrast CT (NCCT). We aimed to assess the performance of the e-Stroke Suite software (Brainomix, Oxford, United Kingdom) in assessing ischemic core volumes on NCCT compared to CTP in patients with acute ischemic stroke. Methods: In this retrospective study, consecutive patients with anterior circulation large vessel occlusions who underwent pretreatment NCCT and CTP and posttreatment MRI were included. Ischemic core volumes were automatically calculated on NCCTs using e-Stroke Suite (Brainomix) which uses a combination of traditional 3D graphics and ML classification techniques to identify ischemic core voxels. Estimated ischemic core volumes were also automatically calculated from CTP using Olea Sphere (Olea Medical SAS, SP23) using a combination of rCBF<25% and differential Time-to-peak (dTTP)>5 sec. Estimated core volumes were compared against the final infarct volume on posttreatment MRI in patients who achieved successful reperfusion (mTICI ≥2b). Results: 83 patients [52 female; age (mean ± SD): 73.1 ±15.3] were included. The estimated ischemic core volumes (mean ± SD) were 18.9 ± 13.5 mL on NCCT and 17.5 ± 16.5 mL on CTP, not significantly different (p=0.54) and demonstrated significant correlation (r=0.51, p<0.001) ( Figure 1 ). Among patients with successful recanalization (n=49), there was no significant difference in estimated ischemic core volume between NCCT vs. CTP (p=0.80) and NCCT vs. MRI (p=0.38). There was significant correlation between estimated ischemic core volume on NCCT vs. CTP (r=0.75, p<0.001) and vs. final MRI infarct volume (r=0.75, p<0.001). Conclusions: Results show estimated ischemic core volumes obtained automatically by ML-based approach (Brainomix) on NCCT correlates well with ischemic core volumes on acute CTP and with post-treatment MR infarct volume.

Comprehensive review of supervised machine learning algorithms to identify the best and error free

Supervised classification is one of the tasks most frequently carried out by the intelligent systems. Supervised Machine Learning (SML) is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. This paper; compares various supervised. Seven different machine learning algorithms were considered: Decision Table, Random Forest (RF) , Naïve Bayes (NB) , Support Vector Machine (SVM), Neural Networks (Perceptron), JRip and Decision Tree (J48)l. And also reviews various Supervised Machine Learning (ML) classification techniques with the aim of identifying the Best and Error free algorithm.

An Efficient Two-Stage Network Intrusion Detection System in the Internet of Things

Internet of Things (IoT) devices and services provide convenience but face serious security threats. The network intrusion detection system is vital in ensuring the security of the IoT environment. In the IoT environment, we propose a novel two-stage intrusion detection model that combines machine learning and deep learning to deal with the class imbalance of network traffic data and achieve fine-grained intrusion detection on large-scale flow data. The superiority of the model is verified on the newer and larger CSE-CIC-IDS2018 dataset. In Stage-1, the LightGBM algorithm recognizes normal and abnormal network traffic data and compares six classic machine learning techniques. In Stage-2, the Convolutional Neural Network (CNN) performs fine-grained attack class detection on the samples predicted to be abnormal in Stage-1. The Stage-2 multiclass classification achieves a detection rate of 99.896%, F1score of 99.862%, and an MCC of 95.922%. The total training time of the two-stage model is 74.876 s. The detection time of a sample is 0.0172 milliseconds. Moreover, we set up an optional Synthetic Minority Over-sampling Technique based on the imbalance ratio (IR-SMOTE) of the dataset in Stage-2. Experimental results show that, compared with SMOTE technology, the two-stage intrusion detection model can adapt to imbalanced datasets well and reveal higher efficiency and better performance when processing large-scale flow data, outperforming state-of-the-art intrusion detection systems.

An integrated machine learning and DEMATEL approach for feature preference and purchase intention modelling

This article models the purchasing intention of young Indian consumers for branded smartphones by analysing their individual preferences for the different features of the smartphone. This article selects thirteen features for analysis, and the respondents are asked to rate, which are further used to model the mentioned purchasing intention. The features are selected using three quantitative methods: uncertainty calculation, correlation and Decision-Making Trial and Evaluation Laboratory (DEMATEL). The uncertainty indicates the variance in the respondents’ preference for a particular feature. So, the features with highly uncertain preference values are undesired. The correlation analysis indicates the feature preferences impacting the buying intention of a branded smartphone. The hierarchy and the causality for the impact of feature preferences on the branded smartphone preference are identified based on DEMATEL. The selected features are used to model the purchasing intention based on K nearest neighbour (KNN), a machine learning technique. The result is compared with the other three machine learning techniques for classification, namely, C4.5, CART, and logistic regression. The result indicates the superiority of KNN based model, which is further used for building a brand switching intention model.

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development.

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.

EasyPheno: An easy-to-use and easy-to-extend Python framework for phenotype prediction using Bayesian optimization.

Predicting complex traits from genotypic information is a major challenge in various biological domains. With easyPheno, we present a comprehensive Python framework enabling the rigorous training, comparison and analysis of phenotype predictions for a variety of different models, ranging from common genomic selection approaches over classical machine learning and modern deep learning-based techniques. Our framework is easy-to-use, also for non-programming-experts, and includes an automatic hyperparameter search using state-of-the-art Bayesian optimization. Moreover, easyPheno provides various benefits for bioinformaticians developing new prediction models. easyPheno enables to quickly integrate novel models and functionalities in a reliable framework and to benchmark against various integrated prediction models in a comparable setup. In addition, the framework allows the assessment of newly developed prediction models under pre-defined settings using simulated data. We provide a detailed documentation with various hands-on tutorials and videos explaining the usage of easyPheno to novice users. easyPheno is publicly available at https://github.com/grimmlab/easyPheno and can be easily installed as Python package via https://pypi.org/project/easypheno/ or using Docker. A comprehensive documentation including various tutorials complemented with videos can be found at https://easypheno.readthedocs.io/. Supplementary data are available at Bioinformatics Advances online.

Exploring the incremental utility of circulating biomarkers for robust risk prediction of incident atrial fibrillation in European cohorts using regressions and modern machine learning methods.

To identify robust circulating predictors for incident atrial fibrillation (AF) using classical regressions and machine learning (ML) techniques within a broad spectrum of candidate variables. In pooled European community cohorts (n = 42 280 individuals), 14 routinely available biomarkers mirroring distinct pathophysiological pathways including lipids, inflammation, renal, and myocardium-specific markers (N-terminal pro B-type natriuretic peptide [NT-proBNP], high-sensitivity troponin I [hsTnI]) were examined in relation to incident AF using Cox regressions and distinct ML methods. Of 42 280 individuals (21 843 women [51.7%]; median [interquartile range, IQR] age, 52.2 [42.7, 62.0] years), 1496 (3.5%) developed AF during a median follow-up time of 5.7 years. In multivariable-adjusted Cox-regression analysis, NT-proBNP was the strongest circulating predictor of incident AF [hazard ratio (HR) per standard deviation (SD), 1.93 (95% CI, 1.82-2.04); P < 0.001]. Further, hsTnI [HR per SD, 1.18 (95% CI, 1.13-1.22); P < 0.001], cystatin C [HR per SD, 1.16 (95% CI, 1.10-1.23); P < 0.001], and C-reactive protein [HR per SD, 1.08 (95% CI, 1.02-1.14); P = 0.012] correlated positively with incident AF. Applying various ML techniques, a high inter-method consistency of selected candidate variables was observed. NT-proBNP was identified as the blood-based marker with the highest predictive value for incident AF. Relevant clinical predictors were age, the use of antihypertensive medication, and body mass index. Using different variable selection procedures including ML methods, NT-proBNP consistently remained the strongest blood-based predictor of incident AF and ranked before classical cardiovascular risk factors. The clinical benefit of these findings for identifying at-risk individuals for targeted AF screening needs to be elucidated and tested prospectively.

Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction

Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients' survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area.

Machine Learning Hybrid Model for the Prediction of Chronic Kidney Disease.

To diagnose an illness in healthcare, doctors typically conduct physical exams and review the patient's medical history, followed by diagnostic tests and procedures to determine the underlying cause of symptoms. Chronic kidney disease (CKD) is currently the leading cause of death, with a rapidly increasing number of patients, resulting in 1.7 million deaths annually. While various diagnostic methods are available, this study utilizes machine learning due to its high accuracy. In this study, we have used the hybrid technique to build our proposed model. In our proposed model, we have used the Pearson correlation for feature selection. In the first step, the best models were selected on the basis of critical literature analysis. In the second step, the combination of these models is used in our proposed hybrid model. Gaussian Naïve Bayes, gradient boosting, and decision tree classifier are used as a base classifier, and the random forest classifier is used as a meta-classifier in the proposed hybrid model. The objective of this study is to evaluate the best machine learning classification techniques and identify the best-used machine learning classifier in terms of accuracy. This provides a solution for overfitting and achieves the highest accuracy. It also highlights some of the challenges that affect the result of better performance. In this study, we critically review the existing available machine learning classification techniques. We evaluate in terms of accuracy, and a comprehensive analytical evaluation of the related work is presented with a tabular system. In implementation, we have used the top four models and built a hybrid model using UCI chronic kidney disease dataset for prediction. Gradient boosting achieves around 99% accuracy, random forest achieves 98%, decision tree classifier achieves 96% accuracy, and our proposed hybrid model performs best getting 100% accuracy on the same dataset. Some of the main machine learning algorithms used to predict the occurrence of CKD are Naïve Bayes, decision tree, K-nearest neighbor, random forest, support vector machine, LDA, GB, and neural network. In this study, we apply GB (gradient boosting), Gaussian Naïve Bayes, and decision tree along with random forest on the same set of features and compare the accuracy score.