Prediction for chronic kidney disease by categorical and non_categorical attributes using different machine learning algorithms.

Chronic kidney disease (CKD) is a common disease as it is difficult to diagnose early due to its lack of symptoms. The main goal is to first diagnose kidney failure, which is a requirement for dialysis or a kidney transplant. This model teaches patients how to live a healthy life, helps doctors identify the risk and severity of disease, and how plan future treatments. Machine learning algorithms are often used in health care to predict and manage the disease. The purpose of this study is to develop a model for the early detection of CKD, which has three parts: (a) applying baseline classifiers on categorical attributes, (b) applying baseline classifiers on non_categorical attributes, (c) applying baseline classifiers on both categorical and non_categorical attributes, and (d) improving the results of the proposed model by combing the results of above three classifiers based on a majority vote. The proposed model based on baseline classifiers and the majority voting method shows a 3% increase in accuracy over the other existing models. The results provide support for increased accuracy in the current classification of chronic kidney disease.

Antimicrobial peptide moricin induces ROS mediated caspase-dependent apoptosis in human triple-negative breast cancer via suppression of notch pathway.

BACKGROUND: Breast cancer is the world's most prevalent cancer among women. Microorganisms have been the richest source of antibiotics as well as anticancer drugs. Moricin peptides have shown antibacterial properties; however, the anticancer potential and mechanistic insights into moricin peptide-induced cancer cell death have not yet been explored. METHODS: An investigation through in silico analysis, analytical methods (Reverse Phase-High Performance Liquid Chromatography (RP-HPLC), mass spectroscopy (MS), circular dichroism (CD), and in vitro studies, has been carried out to delineate the mechanism(s) of moricin-induced cancer cell death. An in-silico analysis was performed to predict the anticancer potential of moricin in cancer cells using Anti CP and ACP servers based on a support vector machine (SVM). Molecular docking was performed to predict the binding interaction between moricin and peptide-related cancer signaling pathway(s) through the HawkDOCK web server. Further, in vitro anticancer activity of moricin was performed against MDA-MB-231 cells. RESULTS: In silico observation revealed that moricin is a potential anticancer peptide, and protein-protein docking showed a strong binding interaction between moricin and signaling proteins. CD showed a predominant helical structure of moricin, and the MS result determined the observed molecular weight of moricin is 4544 Da. An in vitro study showed that moricin exposure to MDA-MB-231 cells caused dose dependent inhibition of cell viability with a high generation of reactive oxygen species (ROS). Molecular study revealed that moricin exposure caused downregulation in the expression of Notch-1, NF-ƙB and Bcl2 proteins while upregulating p53, Bax, caspase 3, and caspase 9, which results in caspase-dependent cell death in MDA-MB-231 cells. CONCLUSIONS: In conclusion, this study reveals the anticancer potential and underlying mechanism of moricin peptide-induced cell death in triple negative cancer cells, which could be used in the development of an anticancer drug.

Improved detection of dry age-related macular degeneration from optical coherence tomography images using adaptive window based feature extraction and weighted ensemble based classification approach.

BACKGROUND: Dry Age-related macular degeneration (AMD), which affects the older population, can lead to blindness when left untreated. Preventing vision loss in elderly needs early identification. Dry-AMD diagnosis is still time-consuming and very subjective, depending on the ophthalmologist. Setting up a thorough eye-screening system to find Dry-AMD is a very difficult task. METHODOLOGY: This study aims to develop a weighted majority voting (WMV) ensemble-based prediction model to diagnose Dry-AMD. The WMV approach combines the predictions from base-classifiers and chooses the class with greatest vote based on assigned weights to each classifier. A novel feature extraction method is used along the retinal pigment epithelium (RPE) layer, with the number of windows calculated for each picture playing an important part in identifying Dry-AMD/normal images using the WMV methodology. Pre-processing using hybrid-median filter followed by scale-invariant feature transform based segmentation of RPE layer and curvature flattening of retina is employed to measure exact thickness of RPE layer. RESULT: The proposed model is trained on 70% of the OCT image database (OCTID) and evaluated on remaining OCTID and SD-OCT Noor dataset. Model has achieved accuracy of 96.15% and 96.94%, respectively. The suggested algorithm's effectiveness in Dry-AMD identification is demonstrated by comparison with alternative approaches. Even though the suggested model is only trained on the OCTID, it has performed well when tested on additional dataset. CONCLUSION: The suggested architecture can be used for quick eye-screening for early identification of Dry-AMD. The recommended method may be applied in real-time since it requires fewer complexity and learning-variables.

Improved detection accuracy of red lesions in retinal fundus images with superlearning approach.

BACKGROUND: Diabetic Retinopathy (DR) is a serious consequence of diabetes that can result to permanent vision loss for a person. Diabetes-related vision impairment can be significantly avoided with timely screening and treatment in its initial phase. The earliest and the most noticeable indications on the surface of the retina are micro-aneurysm and haemorrhage, which appear as dark patches. Therefore, the automatic detection of retinopathy begins with the identification of all these dark lesions. METHOD: In our study, we have developed a clinical knowledge based segmentation built on Early Treatment DR Study (ETDRS). ETDRS is a gold standard for identifying all red lesions using adaptive-thresholding approach followed by different pre-processing steps. The lesions are classified using super-learning approach to improve multi-class detection accuracy. Ensemble based super-learning approach finds optimal weights of base learners by minimizing the cross validated risk-function and it pledges the improved performance compared to base-learners predictions. For multi-class classification, a well informative feature-set based on colour, intensity, shape, size and texture, is developed. In this work, we have handled the data imbalance problem and compared the final accuracy with different synthetic data creation ratios. RESULT: The suggested approach uses publicly available resources to perform quantitative assessments at lesions-level. The overall accuracy of red lesion segregation is 93.5%, which has increased to 97.88% when data imbalance problem is taken care-off. CONCLUSION: The results of our system have achieved competitive performance compared with other modern approaches and handling of data imbalance further increases the performance of it.

Exploring the Role of Glycolytic Enzymes PFKFB3 and GAPDH in the Modulation of Aß and Neurodegeneration and Their Potential of Therapeutic Targets in Alzheimer's Disease.

Alzheimer's disease (AD) is presently the 6th major cause of mortality across the globe. However, it is expected to rise rapidly, following cancer and heart disease, as a leading cause of death among the elderly peoples. AD is largely characterized by metabolic changes linked to glucose metabolism and age-induced mitochondrial failure. Recent research suggests that the glycolytic pathway is required for a range of neuronal functions in the brain including synaptic transmission, energy production, and redox balance; however, alteration in glycolytic pathways may play a significant role in the development of AD. Moreover, it is hypothesized that targeting the key enzymes involved in glucose metabolism may help to prevent or reduce the risk of neurodegenerative disorders. One of the major pro-glycolytic enzyme is 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3); it is normally absent in neurons but abundant in astrocytes. Similarly, another key of glycolysis is glyceraldehyde-3-phosphate dehydrogenase (GAPDH) which catalyzes the conversion of aldolase and glyceraldehyde 3 phosphates to 1,3 bisphosphoglycerate. GAPDH has been reported to interact with various neurodegenerative disease-associated proteins, including the amyloid-ß protein precursor (AßPP). These findings indicate PFKFB3 and GAPDH as a promising therapeutic target to AD. Current review highlight the contributions of PFKFB3 and GAPDH in the modulation of Aßand AD pathogenesis and further explore the potential of PFKFB3 and GAPDH as therapeutic targets in AD.

Serum and urine metabolomics analysis reveals the role of altered metabolites in patulin-induced nephrotoxicity.

Various studies have identified the kidney as a target organ for patulin (PAT)-induced toxicity. However, detailed mechanistic insights into PAT-induced nephrotoxicity had not yet been done. Therefore, along with classical toxicological parameters, liquid chromatography-high resolution massed spectrometry (LC-HRMS) based metabolomics has been carried out to delineate the mechanism(s) of PAT-induced nephrotoxicity.An in vivo study was conducted using male Wistar rats, divided into three groups. PAT (25 µg/kg b.wt and 100 µg/kg b.wt) and, control were given through oral gavage, 5 days/week for 28 days. At the end of the experiment, changes in the mean body/ organ weight, food and water intake, expression of marker proteins of kidney injury, and histopathological changes were investigated. Furthermore, using LC-HRMS based metabolomics was performed on the serum and urine of PAT-exposed rats. The histopathological and toxicological analysis revealed a significant increase in glomerular mesangial cells, vacuolar degeneration, and cast deposition in the proximal convoluted tubules. The metabolomics showed metabolic perturbations in amino and fatty acid-related metabolic pathways in serum and urine of PAT-treated rats. In conclusion this study expands our understanding of PAT-induced metabolic alterations and its effects on renal function.

Ochratoxin A treated rat derived urinary exosomes enhanced cell growth and extracellular matrix production in normal kidney cells through modulation of TGF-ß1/smad2/3 signaling pathway.

AIMS: Kidney is the main target organ for ochratoxin A (OTA) toxicity; however, the mechanism(s) involved in OTA-induced nephrotoxicity is not fully understood. Recently, exosomes, nano-sized vesicles have been found to play an important role in promotion and progression of disease as well as environmental toxicant-induced patho-physiology of toxicity. Hence, we aimed to investigate the role of exosomes in OTA-mediated nephrotoxicity. MAIN METHODS: Male Wistar rats were divided in to two groups. Rats of one group were treated with OTA (210 µg/kg b. wt) and another with vehicle control through oral gavage (5 days/week) for 270 days. At the end of experiment, exosomes concentrations from rat's urine were measured. To examine the OTA-induced nephrotoxicity, histopathology was performed using H & E, Masson's trichome and PAS staining. For mechanistic study, normal rat kidney (NRK52E) cells were exposed with either vehicles treated rat's urinary exosomes (NEx) or OTA treated rat's urinary exosomes (OEx) and effects on cell proliferation, cell growth, extracellular matrix production and TGF-ß1/smad2/3 pathway was analyzed. KEY FINDINGS: OTA treatment to Wistar rats caused histopathological changes such as tubular degeneration, glomeruli shrinkage and hypercellularity in kidney tissue. Interestingly, OTA treated rat's urine has more exosomes secretion. Moreover, treatment of NRK52E cells with OEx caused increased cell proliferation, cell growth, enhanced the expression of extracellular matrix proteins and activation of TGF-ß1/smad2/3 pathway. SIGNIFICANCE: Our investigations exogenous exposure of OTA derived urinary exosomes caused TGF-ß1/smad2/3 pathway-mediated activation of pro-fibrotic changes in kidney will helpful for deeper understanding the OTA-induced nephrotoxicity.

DNA Methylation Biomarkers-Based Human Age Prediction Using Machine Learning.

PURPOSE: Age can be an important clue in uncovering the identity of persons that left biological evidence at crime scenes. With the availability of DNA methylation data, several age prediction models are developed by using statistical and machine learning methods. From epigenetic studies, it has been demonstrated that there is a close association between aging and DNA methylation. Most of the existing studies focused on healthy samples, whereas diseases may have a significant impact on human age. Therefore, in this article, an age prediction model is proposed using DNA methylation biomarkers for healthy and diseased samples. METHODS: The dataset contains 454 healthy samples and 400 diseased samples from publicly available sources with age (1-89 years old). Six CpG sites are identified from this data having a high correlation with age using Pearson's correlation coefficient. In this work, the age prediction model is developed using four different machine learning techniques, namely, Multiple Linear Regression, Support Vector Regression, Gradient Boosting Regression, and Random Forest Regression. Separate models are designed for healthy and diseased data. The data are split randomly into 80 : 20 ratios for training and testing, respectively. RESULTS: Among all the techniques, the model designed using Random Forest Regression shows the best performance, and Gradient Boosting Regression is the second best model. In the case of healthy samples, the model achieved a MAD of 2.51 years for training data and 4.85 for testing data. Also, for diseased samples, a MAD of 3.83 years is obtained for training and 9.53 years for testing. CONCLUSION: These results showed that the proposed model can predict age for healthy and diseased samples.

Transcriptomic and proteomic insights into patulin mycotoxin-induced cancer-like phenotypes in normal intestinal epithelial cells.

Patulin (PAT) is a natural contaminant of fruits (primarily apples) and their products. Significantly, high levels of contamination have been found in fruit juices all over the world. Several in vitro studies have demonstrated PAT's ability to alter intestinal structure and function. However, in real life, the probability of low dose long-term exposure to PAT to humans is significantly higher through contaminated food items. Thus, in the present study, we have exposed normal intestinal cells to non-toxic levels of PAT for 16 weeks and observed that PAT had the ability to cause cancer-like properties in normal intestinal epithelial cells after chronic exposure. Here, our results showed that chronic exposure to low doses of PAT caused enhanced proliferation, migration and invasion ability, and the capability to grow in soft agar (anchorage independence). Moreover, an in vivo study showed the appearance of colonic aberrant crypt foci (ACFs) in PAT-exposed Wistar rats, which are well, establish markers for early colon cancer. Furthermore, as these neoplastic changes are consequences of alterations at the molecular level, here, we combined next-generation RNA sequencing with liquid chromatography mass spectrometry-based proteomic analysis to investigate the possible underlying mechanisms involved in PAT-induced neoplastic changes.

Study of a modified non-contact Hall sensor-based flow transducer using centrifugal and momentum force principle.

The paper describes a modified non-contact Hall sensor based centrifugal and momentum force type flow transducer with simple design, low cost and rugged construction. In this transducer two identical permanent magnets are fixed on the two ends of the common balance lever on which sensing U-tube and dummy U-tube are placed at equal distances from the pivot. The movement of lever ends with variation of flow rate, changes the magnet positions with respect to the two identical Hall sensors fixed at two locations just below the magnets. Thus the outputs of the Hall sensors supplied from a stabilized DC source vary with the variation of flow rate. The difference between these outputs is a DC voltage signal non-linearly related with flow rate.

TGF-ß/Smad signaling pathway plays a crucial role in patulin-induced pro-fibrotic changes in rat kidney via modulation of slug and snail expression.

Patulin (PAT) is a mycotoxin that contaminates a variety of food and foodstuffs. Earlier in vitro and in vivo findings have indicated that kidney is one of the target organs for PAT-induced toxicity. However, no study has evaluated the chronic effects of PAT exposure at environmentally relevant doses or elucidated the detailed mechanism(s) involved. Here, using in vitro and in vivo experimental approaches, we delineated the mechanism/s involved in pro-fibrotic changes in the kidney after low-dose chronic exposure to PAT. We found that non-toxic concentrations (50 nM and 100 nM) of PAT to normal rat kidney cells (NRK52E) caused a higher generation of reactive oxygen species (ROS) (mainly hydroxyl (•OH), peroxynitrite (ONOO-), and hypochlorite radical (ClO-). PAT exposure caused the activation of mitogen-activated protein kinases (MAPKs) and its downstream c-Jun/Fos signaling pathways. Moreover, our chromatin immunoprecipitation (ChIP) analysis suggested that c-Jun/Fos binds to the promoter region of Transforming growth factor beta (TGF-ß1) and possibly induces its expression. Results showed that PAT-induced TGF-ß1 further activates the TGF-ß1/smad signaling pathways. Higher activation of slug and snail transcription factors further modulates the regulation of pro-fibrotic molecules. Similarly, in vivo results showed that PAT exposure to rats through gavage at 25 and 100 µg/kg b. wt had higher levels of kidney injury/toxicity markers namely vascular endothelial growth factor (VEGF), kidney Injury Molecule-1 (Kim-1), tissue inhibitor of metalloproteinase-1 (Timp-1), and clusterin (CLU). Additionally, histopathological analysis indicated significant alterations in renal tubules and glomeruli along with collagen deposition in PAT-treated rat kidneys. Overall, our data provide evidence of the involvement of ROS mediated MAPKs and TGF-ß1/smad pathways in PAT-induced pro-fibrotic changes in the kidney via modulation of slug and snail expression.

Learner question's correctness assessment and a guided correction method: enhancing the user experience in an interactive online learning system.

In an interactive online learning system (OLS), it is crucial for the learners to form the questions correctly in order to be provided or recommended appropriate learning materials. The incorrect question formation may lead the OLS to be confused, resulting in providing or recommending inappropriate study materials, which, in turn, affects the learning quality and experience and learner satisfaction. In this paper, we propose a novel method to assess the correctness of the learner's question in terms of syntax and semantics. Assessing the learner's query precisely will improve the performance of the recommendation. A tri-gram language model is built, and trained and tested on corpora of 2,533 and 634 questions on Java, respectively, collected from books, blogs, websites, and university exam papers. The proposed method has exhibited 92% accuracy in identifying a question as correct or incorrect. Furthermore, in case the learner's input question is not correct, we propose an additional framework to guide the learner leading to a correct question that closely matches her intended question. For recommending correct questions, soft cosine based similarity is used. The proposed framework is tested on a group of learners' real-time questions and observed to accomplish 85% accuracy.

COVID-19 Pandemic: ARIMA and Regression Model-Based Worldwide Death Cases Predictions.

COVID-19 has now taken a frightening form. As the days pass, it is becoming more and more widespread and now it has become an epidemic. The death rate, which was earlier in the hundreds, changed to thousands and then progressed to millions. If the same situation persists over time, the day is not far when the humanity of all the countries on the globe will be endangered and we yearn for breath. From January 2020 till now, many scientists, researchers and doctors have been trying to solve this complex problem so that proper arrangements can be made by the governments in the hospitals and the death rate can be reduced. The presented research article shows the estimated mortality rate by the ARIMA model and the regression model. This dataset has been collected precisely from DataHub-Novel Coronavirus 2019-Dataset from 22nd January to 29th June 2020. To show the current mortality rate of the entire subject, the correlation coefficients of attributes (MAE, MSE, RMSE and MAPE) were used, where the average absolute percentage error validated the model by 99.09%. The ARIMA model is used to generate auto_arima SARIMAX results, auto_arima residual plots, ARIMA model results, and corresponding prediction plots on the training dataset. These data indicate a continuous decline in death cases. By applying a regression model, the coefficients generated by the regression model are estimated, and the actual death cases and expected death cases are compared and analyzed. It is found that the predicted mortality rate has decreased after May 2, 2020. It will help the government and doctors prepare for the forthcoming plans. Based on short-period predictions, these methods can be used to forecast the mortality rate for a long period.

Acoustic feature based unsupervised approach of heart sound event detection.

This paper represents an unsupervised approach to detect the positions of S1, S2 heart sound events in a Phonocardiogram (PCG) recording. Insufficiency of correctly annotated heart sound database drives us to investigate unsupervised techniques. Gammatone filter bank features are used to characterize the spectral pattern of fundamental heart sound events from noise contaminated PCG data. An unsupervised spectral clustering technique is employed for segmentation of S1/S2 and non-S1/S2 heart sound events. A Feature winning score is computed to identify the S1/S2 and non-S1/S2 frames. Finally, time based threshold is applied to detect the accurate positions of S1 and S2 heart sounds. The performance of spectral clustering is compared with other clustering methods. The proposed method offers a maximum F1-score of 98% and 92.5% for normal and abnormal PCG data respectively on 2016 PhysioNet/CinC challenge dataset. The heart sound annotation algorithm provided by PhysioNet has been used as the ground truth after hand correction.

Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method-a Comparative Study.

Nowadays, skin disease is a major problem among peoples worldwide. Different machine learning techniques are applied to predict the various classes of skin disease. In this research paper, we have applied six different machine learning algorithm to categorize different classes of skin disease using three ensemble techniques and then a feature selection method to compare the results obtained from different machine learning techniques. In the proposed study, we present a new method, which applies six different data mining classification techniques and then developed an ensemble approach using bagging, AdaBoost, and gradient boosting classifiers techniques to predict the different classes of skin disease. Further, the feature importance method is used to select important 15 features which play a major role in prediction. A subset of the original dataset is obtained after selecting only 15 features to compare the results of used six machine learning techniques and ensemble approach as on the whole dataset. The ensemble method used on skin disease dataset is compared with the new subset of the original dataset obtained from feature selection method. The outcome shows that the dermatological prediction accuracy of the test dataset is increased compared with an individual classifier and a better accuracy is obtained as compared with subset obtained from feature selection method. The ensemble method and feature selection used on dermatology datasets give better performance as compared with individual classifier algorithms. Ensemble method gives more accurate and effective skin disease prediction.

Prediction of Skin Disease with Three Different Feature Selection Techniques Using Stacking Ensemble Method.

Skin disease is the most common problem between people. Due to pollution and deployment of ozone layer, harmful UV rays of sun burn the skin and develop various types of skin diseases. Nowadays, machine learning and deep learning algorithms are generally used for diagnosis for various kinds of diseases. In this study, we have applied three feature extraction techniques univariate feature selection, feature importance, and correlation matrix with heat map to find the optimum data subset of erythemato-squamous disease. Four classification techniques Gaussian Naïve Bayesian (NB), decision tree (DT), support vector machine (SVM), and random forest are used for measuring the performance of model. Stacking ensemble technique is then applied to enhance the prediction performance of the model. The proposed method used for measuring the performance of the model. It is finding that the optimal subset of the erythemato-squamous disease is performed well in the case of correlation and heat map feature selection techniques. The mean value, slandered deviation, root mean square error, kappa statistical error, and area under receiver operating characteristics and accuracy are calculated for demonstrating the effectiveness of the proposed model. The feature selection techniques applied with staking ensemble technique gives the better result as compared to individual machine learning techniques. The obtained results show that the performance of proposed model is higher than previous results obtained by researchers.

A novel approach towards non-obstructive detection and classification of COPD using ECG derived respiration.

The alarming rate of mortality and disability due to Chronic Obstructive Pulmonary Disease (COPD) has become a serious health concern worldwide. The progressive nature of this disease makes it inevitable to detect this disease in its early stages, leads to a greater demand for developing non-obstructive and reliable technology for COPD detection. The use of highly patient-effort dependent, time-consuming, and expensive methods are some major inherent limitations of previous techniques. Lack of knowledge about the disease and inadequacy of proper diagnostic tool for early detection of COPD is another reason behind the 3rd leading cause of death worldwide. For this reason, this study aims to explore the utility of ECG Derived Respiration (EDR) for classification between COPD patients and normal healthy subjects as EDR can be easily extracted from ECG. ECG and respiration signals collected from 30 normal and 30 COPD subjects were analysed. Error calculation and statistical analysis were performed to observe the similarity between original respiration and EDR signal. The morphological pattern changes of respiration and EDR signals were analysed and three different features were extracted from those. Classification was performed by different classifiers employing Decision Tree, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Apart from obtaining comparable classification performance it was seen that EDR has better potential than the original respiration signal for classification of COPD from normal population.

Classification of Skin Disease using Ensemble Data Mining Techniques.

Objective: Skin diseases are a major global health problem associated with high number of people. With the rapid development of technologies and the application of various data mining techniques in recent years, the progress of dermatological predictive classification has become more and more predictive and accurate. Therefore, development of machine learning techniques, which can effectively differentiate skin disease classification, is of vast importance. The machine learning techniques applied to skin disease prediction so far, no techniques outperforms over all the others. Methods: In this research paper, we present a new method, which applies five different data mining techniques and then developed an ensemble approach that consists all the five different data mining techniques as a single unit. We use informative Dermatology data to analysis different data mining techniques to classify the skin disease and then, an ensemble machine learning method is applied. Results: The proposed ensemble method, which is based on machine learning was tested on Dermatology datasets and classify the type of skin disease in six different classes like include C1: psoriasis, C2: seborrheic dermatitis, C3: lichen planus, C4: pityriasis rosea, C5: chronic dermatitis, C6: pityriasis rubra. The results show that the dermatological prediction accuracy of the test data set is increased compared to a single classifier. Conclusion: The ensemble method used on Dermatology datasets give better performance as compared to different classifier algorithms. Ensemble method gives more accurate and effective skin disease prediction.

To Generate an Ensemble Model for Women Thyroid Prediction Using Data Mining Techniques

Objective: The main objective of this paper is to easily identify thyroid symptom for treatment. Methods: In this paper two main techniques are proposed for mining the hidden pattern in the dataset. Ensemble-I and Ensemble- II both are machine learning techniques. Ensemble-I generated from decision tree, over fitting and neural network and Ensemble-II generated from combinations of Bagging and Boosting techniques. Finally proposed experiment is conducted by Ensemble-I vs. Ensemble-II. Results: In the entire experimental setup find an ensemble ­II generated model is the higher compare to other ensemble-I model. In each experiment observe and compare the value of all the performance of ROC, MAE, RMSE, RAE and RRSE. Stacking (ensemble-I) ensemble model estimate the weights for input with output model by thyroid dataset. After the measurement find out the results ROC=(98.80), MAE= (0.89), 6RMSE=(0.21), RAE= (52.78), RRSE=(83.71)and in the ensemble-II observe thyroid dataset and measure all performance of the model ROC=(98.79), MAE= (0.31), RMSE=(0.05) and RAE= (35.89) and RRSE=(52.67). Finally concluded that (Bagging+ Boosting) ensemble-II model is the best compare to other.