Wisconsin Diagnostic Breast Cancer Dataset Research Articles

Improving Breast Cancer Diagnosis Accuracy by Particle Swarm Optimization Feature Selection

Breast cancer has been one of the leading causes of death among women in the world. Early detection of this disease can save patient’s lives and reduce mortality. Due to the large number of features involved in the diagnosis of this disease, the breast cancer diagnosis process can be time consuming. To reduce cost and time and improving accuracy of breast cancer diagnosis, this paper propose a feature selection algorithm based on particle swarm optimization (PSO) combined with machine learning methods for selection the most effective features for breast cancer diagnosis among all features. In order to evaluate the efficiency of the proposed feature selection method, it was tested on three most common breast cancer datasets available in the University of California, Irvine (UCI) repository named: Coimbra dataset (CD), Wisconsin Diagnostic Breast Cancer dataset (WDBC) and Wisconsin Prognostic Breast Cancer dataset (WPBC). In the Coimbra dataset with all its 9 features and without PSO feature selection algorithm the highest obtained accuracy was 87% by Support Vector Machine method, while with PSO feature selection algorithm the accuracy reached to 91% and the number of features was reduced from 9 to 4. In the WDBC dataset with all its 30 features and without PSO feature selection algorithm the highest obtained accuracy was 99% by Random Forest method, while with PSO feature selection algorithm the accuracy reached to 100% and the number of features was reduced from 30 to 19. In the WPBC dataset with all its 33 features and without PSO feature selection algorithm the highest obtained accuracy was 94% by Support Vector Machine method, while with PSO feature selection algorithm the accuracy reached to 96% and the number of features was reduced from 33 to 17. The results of this paper indicated that the proposed feature selection algorithm based on PSO algorithm can improve the accuracy of breast cancer diagnosis. While it has selected fewer and more effective features than the total number of features in the original datasets.

An enhanced soft-computing based strategy for efficient feature selection for timely breast cancer prediction: Wisconsin Diagnostic Breast Cancer dataset case

An enhanced soft-computing based strategy for efficient feature selection for timely breast cancer prediction: Wisconsin Diagnostic Breast Cancer dataset case

Comparative analysis of machine learning algorithms for breast cancer prediction

Breast cancer is a global health concern, and early diagnosis is crucial for successful treatment. The objective of this paper is to conduct a comparative analysis of machine-learning algorithms for the prediction of breast cancer. This study used the Wisconsin Diagnostic Breast Cancer Dataset. Data preparation, technique selection, and performance evaluation are included in the study. The inquiry begins by comparing malignant and benign instances according to input factors and diagnostic outcomes. Finding components having an inverse relationship to the diagnosis is prioritized. Next, a careful approach is used to choose attributes to improve the dataset for model construction. The preprocessed data trains and optimizes four well-known machine learning algorithms: Random Forest, Support Vector Machine, K-Nearest Neighbor, and Logistic Regression. The models are evaluated for accuracy, precision, recall, F1-score, and ROC curve. This study aimed to evaluate numerous breast cancer prediction systems to determine their strengths and weaknesses. To provide openness and replicability, the study uses the Jupyter Notebook platform, Python, and data analytic tools. The logistic regression model has a test accuracy percentage of 99.26%, surpassing all other models examined in this study. Furthermore, it has a minimum false positive rate (FPR) of 1 and a false negative rate (FNR) of 4. The model exhibits a higher level of precision in comparison to the studies examined in the literature review. This study is crucial for early diagnosis and therapy development. The effects include lower healthcare expenses, better patient outcomes, and better diagnostics. Machine learning has shown promise in fighting breast cancer, boosting its relevance in healthcare.

Iterative Tuning of Tree-Ensemble-Based Models' parameters Using Bayesian Optimization for Breast Cancer Prediction

The study presents a method for iterative parameter tuning of tree ensemble-based models using Bayesian hyperparameter tuning for states prediction, using breast cancer as an example. The proposed method utilizes three different datasets, including the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the Surveillance, Epidemiology, and End Results (SEER) breast cancer dataset, and the Breast Cancer Coimbra dataset (BCCD), and implements tree ensemble-based models, specifically AdaBoost, Gentle-Boost, LogitBoost, Bag, and RUSBoost, for breast cancer prediction. Bayesian optimization was used to tune the hyperparameters of the models iteratively, and the performance of the models was evaluated using several metrics, including accuracy, precision, recall, and f1-score. Our results show that the proposed method significantly improves the performance of tree ensemble-based models, resulting in higher accuracy, precision, recall, and f1-score. Compared to other state-of-the-art models, the proposed method is more efficient. It achieved perfect scores of 100% for Accuracy, Precision, Recall, and F1-Score on the WDBC dataset. On the SEER BC dataset, the method achieved an accuracy of 95.9%, a precision of 97.6%, a recall of 94.2%, and an F1-Score of 95.9%. For the BCCD dataset, the method achieved an accuracy of 94.7%, a precision of 90%, a recall of 100%, and an F1-Score of 94.7%. The outcomes of this study have important implications for medical professionals, as early detection of breast cancer can significantly increase the chances of survival. Overall, this study provides a valuable contribution to the field of breast cancer prediction using machine learning.

Machine Learning Approaches for Investigating Breast Cancer

ABSTRACT: This study aims to predict whether the case is malignant or benign and concentrate on the anticipated diagnosis; if the case is malignant, it is advised to admit the patient to the hospital for treatment. The primary goal of this work is to put together models in two distinct datasets to predict breast cancer more accurately, faster, and with fewer errors than before. Then contrast the techniques that produced datasets with the highest accuracy. In this study, the datasets were processed using Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbours, Artificial Neural Network, Nave Bayes, Stochastic Gradient Descent (SGD),Gradient boosting classifiers(GBC), Stochastic Gradient Boosting (SGB), Extreme Gradient Boosting (XGBoost),and Random Forest. Two datasets—the Wisconsin Diagnostic Breast Cancer dataset and the Breast Cancer dataset—are used to test these methods. to evaluate the findings and choose the algorithm that is more adept in predicting breast cancer. Seven algorithms that operate on both datasets in the AI platform were used to build the article. Breast cancer prediction has gotten much harder because so many people die from the disease in its early stages. Consequently, using two real-time datasets, one for Wisconsin diagnosis and the other for research on breast cancer. The same methods are applied to both datasets, and it is found that SVM provides the best accuracy in the shortest time and with the lowest error rate.

Using Pearson Correlation and Mutual Information (PC-MI) to Select Features for Accurate Breast Cancer Diagnosis Based on a Soft Voting Classifier

Breast cancer is one of the most critical diseases suffered by many people around the world, making it the most common medical risk they will face. This disease is considered the leading cause of death around the world, and early detection is difficult. In the field of healthcare, where early diagnosis based on machine learning (ML) helps save patients’ lives from the risks of diseases, better-performing diagnostic procedures are crucial. ML models have been used to improve the effectiveness of early diagnosis. In this paper, we proposed a new feature selection method that combines two filter methods, Pearson correlation and mutual information (PC-MI), to analyse the correlation amongst features and then select important features before passing them to a classification model. Our method is capable of early breast cancer prediction and depends on a soft voting classifier that combines a certain set of ML models (decision tree, logistic regression, and support vector machine) to produce one model that carries the strengths of the models that have been combined, yielding the best prediction accuracy. Our work is evaluated by using the Wisconsin Diagnostic Breast Cancer datasets. The proposed methodology outperforms previous work, achieving 99.3% accuracy, an F1 score of 0.9922, a recall of 0.9846, a precision of 1, and an AUC of 0.9923. Furthermore, the accuracy of 10-fold cross-validation is 98.2%.

Comparison of machine learning models for breast cancer diagnosis

<p><span lang="EN-US">Breast cancer is the most common cause of death among women worldwide. Breast cancer can be detected early, and the death rate can be reduced. Machine learning techniques are a hot topic for study and have proved influential in cancer prediction and early diagnosis. This study's objective is to predict and diagnose breast cancer using machine learning models and evaluate the most effective based on six criteria: specificity, sensitivity, precision, accuracy, F1-score and receiver operating characteristic curve. All work is done in the anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries, and pandas and matplotlib. This study used the Wisconsin diagnostic breast cancer dataset to test ten machine learning algorithms: decision tree, linear discriminant analysis, forests of randomized trees, gradient boosting, passive aggressive, logistic regression, naïve Bayes, nearest centroid, support vector machine, and perceptron. After collecting the findings, we performed a performance evaluation and compared these various classification techniques. Gradient boosting model outperformed all other algorithms, scoring 96.77% on the F1-score.</span></p>

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.

Breast cancer classification using a novel hybrid feature selection approach

Many women around the world die due to breast cancer. If breast cancer is treated in the early phase, mortality rates may significantly be reduced. Quite a number of approaches have been proposed to help in the early detection of breast cancer. A novel hybrid feature selection model is suggested in this study. This novel hybrid model aims to build an efficient feature selection method and successfully classify breast lesions. A combination of relief and binary Harris hawk optimization (BHHO) hybrid model is used for feature selection. Then, k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR) and naive Bayes (NB) methods are preferred for the classification task. The suggested hybrid model is tested by three different breast cancer datasets which are Wisconsin diagnostic breast cancer dataset (WDBC), Wisconsin breast cancer dataset (WBCD) and mammographic breast cancer dataset (MBCD). According to the experimental results, the relief and BHHO hybrid model improves the performance of all classification algorithms in all three datasets. For WDBC, relief-BHO-SVM model shows the highest classification rates with an of accuracy of 98.77%, precision of 97.17%, recall of 99.52%, F1-score of 98.33%, specificity of 99.72% and balanced accuracy of 99.62%. For WBCD, relief-BHO-SVM model achieves of accuracy of 99.28%, precision of 98.76%, recall of 99.17%, F1-score of 98.96%, specificity of 99.56% and balanced accuracy of 99.36%. Relief-BHO-SVM model performs the best with an accuracy of 97.44%, precision of 97.41%, recall of 98.26%, F1-score of 97.84%, specificity of 97.47% and balanced accuracy of 97.86% for MBCD. Furthermore, the relief-BHO-SVM model has achieved better results than other known approaches. Compared with recent studies on breast cancer classification, the suggested hybrid method has achieved quite good results.

CED: A case-level explainable paramedical diagnosis via AdaGBDT

Objective:The rapid growth of medical data has greatly promoted the wide exploitation of machine learning for paramedical diagnosis. Inversely proportional to their performance, most machine learning models generally suffer from the lack of explainability, especially the local explainability of the model, that is, the case-specific explainability. Materials and Methods:In this paper, we proposed a GBDT (Gradient Boosting Decision Tree)-based explainable model for case-specific paramedical diagnostics, and mainly make the following contributions: (1) an adaptive gradient boosting decision tree (AdaGBDT) model is proposed to boost the path-mining for decision effectively; (2) to learn a case-specific feature importance embedding for a specific patient, the bi-side mutual information is applied to characterize the backtracking on the decision path; (3) through the collaborative decision-making by globally explainable AdaGBDT with case-based reasoning (CBR) in the case-specific metric space, some hard cases can be identified by the means of visualized interpretation. The performance of our model is evaluated on the Wisconsin diagnostic breast cancer dataset and the UCI heart disease dataset. Results:Experiments conducted on two datasets show that our AdaGBDT achieves the best performance, with the F1-value of 0.9647 and 0.8405 respectively. Moreover, a series of experimental analyses and case studies further illustrate the excellent performance of feature importance embedding. Conclusion:The proposed case-specific explainable paramedical diagnosis via AdaGBDT has excellent predictive performance, with both promising case-level and consistent global explainability.

Classification with 2-D convolutional neural networks for breast cancer diagnosis

Breast cancer is the most common cancer in women. Classification of cancer/non-cancer patients with clinical records requires high sensitivity and specificity for an acceptable diagnosis test. The state-of-the-art classification model—convolutional neural network (CNN), however, cannot be used with such kind of tabular clinical data that are represented in 1-D format. CNN has been designed to work on a set of 2-D matrices whose elements show some correlation with neighboring elements such as in image data. Conversely, the data examples represented as a set of 1-D vectors—apart from the time series data—cannot be used with CNN, but with other classification models such as Recurrent Neural Networks for tabular data or Random Forest. We have proposed three novel preprocessing methods of data wrangling that transform a 1-D data vector, to a 2-D graphical image with appropriate correlations among the fields to be processed on CNN. We tested our methods on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets. To our knowledge, this work is novel on non-image tabular data to image data transformation for the non-time series data. The transformed data processed with CNN using VGGnet-16 shows competitive results for the WBC dataset and outperforms other known methods for the WDBC dataset.

Accuracy Enhancement for Breast Cancer Detection Using Classification and Feature Selection

Chronic disease like kidney failure, heart disease, cancer etc. is the major cause of deaths now days worldwide. Especially for the females the most dangerous type of disease from which the women of every age group are suffering especially the middle age group women’s is the breast cancer. To detect this type of disease at an early stage is a challenging task. In order to predict the breast cancer at an early stage classification algorithm of high accuracy and less error rate are desirable. In this research work we have used 4 classification algorithms K-NN, J48, Logistic regression and Bayes Net for building the predictive model, also the wrapper method of feature selection is used to enhance the accuracy rate and reduce the error rate of the used classifiers. To carry out this research we have used Wisconsin Diagnostic Breast Cancer dataset which contains 569 instances along with 32 attributes and a class attribute which will predict the type of cancer i.e. Benign or Malignant.

Comparison of breast cancer classification models on Wisconsin dataset

Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries as well as matplotlib and Pandas. In this study, the Wisconsin diagnostic breast cancer dataset was used to evaluate eleven machine learning classifiers: decision tree, quadratic discriminant analysis, AdaBoost, Bagging meta estimator, Extra randomized trees, Gaussian process classifier, Ridge, Gaussian nave Bayes, k-Nearest neighbors, multilayer perceptron, and support vector classifier. During performance analysis, extremely randomized trees outperformed all other classifiers with an F1-score of 96.77% after data collection and data analysis.

Blockchain integrated multi-agent system for breast cancer diagnosis

<span>The integration of multi-agent system and blockchain technology can be beneficial to healthcare applications by providing intelligent data analysis with security. This paper presents an architecture that integrates multi-agent learning system and blockchain technology to support breast cancer diagnosis in a secured manner. The proposed system is based on a parallel hybrid fuzzy logic approach for supporting the prediction of breast cancer disease. The proposed system showed a classification accuracy of 96.49% in breast cancer diagnosis when testing with the Wisconsin Diagnostic Breast Cancer dataset. The blockchain is used to provide agent security in the proposed system to ensure that the only trusted and reputed agents are participated in the decision-making process.</span>

Data normalization methods to improve the quality of classification in the breast cancer diagnostic system

In oncology diagnostic systems, images of cells obtained from breast biopsy are often identified by statistical and geometricfea-tures. To classify the values of these features, presented, in particular, in the Wisconsin Diagnostic Breast Cancer dataset,a naive Bayesian classifier, the k-nearest neighbor’smethod, neural networks, and ensembles of decision trees were used in the literature. It is noticed that the classification results obtained with using these methods differ mainly within the limits of the statistical error. This is related to the selection of the classifier which is determined by the shape of the clusters and the presence of data outliers. They are significantly affected by data preparing, in particular, the method of normalization of the feature values. Normalization is defined as transforming the values of features to a certain interval. The difference in the intervals of feature values can lead to implicit weighting of features in their classification. After feature extraction and normalization, a set of data belonging to the same class may be divided into several clusters as a result of feature space distortion. To separate such data into one class, the distance between them must be greater than the internal scatter of data in each of the clusters. Therefore, in addition to normalization, data preparing can include decorrelation and orthogonalization of features, using, e.g., principal component analysiswhich selects feature projections with better class separation. So to improve the quality of classification, in the article the data preparation methods are used, namely data normalization methods and data analysis using principal components. It is shown that it is advisable to use the standard, robust, or minimax normalization of cell feature vectors if the k-nearest neighbor’sclassifier or a naive Bayesian classifier is selected. If the classification of cell feature vectors in breast biopsy images was carried out using an ensemble of decision trees, the use of normali-zation did not improve the quality of the classification.It is advisable to reduce the dimension of the feature space by analyzing the principal components only for the k-nearest method. When using a naive Bayesian classifier and ensembles of decision trees, the transition to principal components reduces the quality of the classification.The results obtainedin the articleallow choosing the pre-paring data methods for a specific problem.

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis.

Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.

Optimized Breast Cancer Classification using Feature Selection and Outliers Detection

Breast cancer is the second most commonly diagnosed cancer in women throughout the world. It is on the rise, especially in developing countries, where the majority of cases are discovered late. Breast cancer develops when cancerous tumors form on the surface of the breast cells. The absence of accurate prognostic models to assist physicians recognize symptoms early makes it difficult to develop a treatment plan that would help patients live longer. However, machine learning techniques have recently been used to improve the accuracy and speed of breast cancer diagnosis. If the accuracy is flawless, the model will be more efficient, and the solution to breast cancer diagnosis will be better. Nevertheless, the primary difficulty for systems developed to detect breast cancer using machine-learning models is attaining the greatest classification accuracy and picking the most predictive feature useful for increasing accuracy. As a result, breast cancer prognosis remains a difficulty in today's society. This research seeks to address a flaw in an existing technique that is unable to enhance classification of continuous-valued data, particularly its accuracy and the selection of optimal features for breast cancer prediction. In order to address these issues, this study examines the impact of outliers and feature reduction on the Wisconsin Diagnostic Breast Cancer Dataset, which was tested using seven different machine learning algorithms. The results show that Logistic Regression, Random Forest, and Adaboost classifiers achieved the greatest accuracy of 99.12%, on removal of outliers from the dataset. Also, this filtered dataset with feature selection, on the other hand, has the greatest accuracy of 100% and 99.12% with Random Forest and Gradient boost classifiers, respectively. When compared to other state-of-the-art approaches, the two suggested strategies outperformed the unfiltered data in terms of accuracy. The suggested architecture might be a useful tool for radiologists to reduce the number of false negatives and positives. As a result, the efficiency of breast cancer diagnosis analysis will be increased.

Computer-aided detection of breast cancer on the Wisconsin dataset: An artificial neural networks approach

The early detection of breast cancer (BC) has a significant impact on reducing the disease’s mortality rate. As an effective cost- and time-saving tool, computer-aided diagnosis (CAD) systems have been developed in this research field, aiding clinicians and radiologists’ decision-making process by offering highly accurate information. In this study, a shallow artificial neural network (ANN) model with one hidden layer is used to diagnose and predict BC using the Wisconsin breast cancer dataset (WBCD) and the Wisconsin diagnostic breast cancer (WDBC) dataset without employing feature optimization or selection algorithms. The datasets are divided into 80% for training and 20% for testing using five-fold cross-validation. The model’s effectiveness and efficiency are evaluated based on sensitivity, specificity, precision, accuracy, and F1 score, along with the area under the receiver-operating characteristic curve (AUC). In the task of classifying benign and malignant tumours using the WBCD, the shallow ANN model showed promising performance with an average accuracy of 99.85%, specificity of 99.72%, sensitivity of 100%, precision of 99.69%, and F1 score of 99.84%. For BC detection using WDBC, it achieved an average accuracy of 99.47%, specificity of 99.53%, sensitivity of 99.59%, precision of 98.71%, and F1 score of 99.13%. The AUC of the proposed model was 99.86% and 99.56% for the WBCD and the WDBC dataset, respectively, illustrating the model’s discrimination capacity. Moreover, the ANN model outperformed state-of-the-art models that integrate feature optimization and selection algorithms to classify BC tumours using WBCD and WDBC. Hence, the shallow ANN model presented here demonstrates significant potential for diagnosing BC using WBCD and WDBC without the need for feature optimization or selection algorithms.

The Investigation of the Success of Different Machine Learning Methods in Breast Cancer Diagnosis

Objective: The aim of this study is to identify cancer earlier in life using machine learning methods.
 
 Methods: For this purpose, the Wisconsin Diagnostic Breast Cancer dataset was classified using Naive Bayes, decision trees, artificial neural networks algorithms and comparison of these machine learning methods was made. KNIME Analytics Platform was used for applications. Before the classification process, the dataset was preprocessed. After the pre-processing stage, three different classifier methods were applied to the dataset. Accuracy, sensitivity, specificity and confusion matrices were used to measure the success of the methods.
 
 Results: The results show that Naive Bayes and artificial neural network methods classify tumors with 96.5% accuracy. The success of the decision tree method in classification was 92.6%.
 
 Conclusion: The machine learning algorithms can be used successfully in breast cancer diagnosis to determine whether the tumors are malign or benign.

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner.

A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate databases. The features selected by each bioinspired algorithm are used to train three back propagation neural networks (BPNN) independently using the conjugate gradient algorithm (CGA). Classifier testing is performed by using the testing set on each trained classifier, and the diagnostic results obtained are used to evaluate the performance of each classifier. The classification results obtained for each instance of the testing set of the three classifiers and the class label associated with each instance of the testing set will be the candidate instances for training and testing the super learner. The training set comprises of 80% of the instances, and the testing set comprises of 20% of the instances. Experimentation has been carried out using seven clinical datasets from the University of California Irvine (UCI) machine learning repository. The super learner has achieved a classification accuracy of 96.83% for Wisconsin diagnostic breast cancer dataset (WDBC), 86.36% for Statlog heart disease dataset (SHD), 94.74% for hepatocellular carcinoma dataset (HCC), 90.48% for hepatitis dataset (HD), 81.82% for vertebral column dataset (VCD), 84% for Cleveland heart disease dataset (CHD), and 70% for Indian liver patient dataset (ILP).