Breast cancer is considered one of the most common cancers occurring in women. Each year, it affects 2.1 million women, causing cancer-related deaths. As per the estimation, breast cancer took the lives of 627,000 women in 2018 alone. The disease is mostly observed in the developed areas of the world, with current rates expanding in almost every region across the world. The role of predicting cancer is crucial for the further progress of data mining tools currently available. K-nearest neighbor, J48 algorithm, and Naïve Bayes are applied to predict cancer disease. For acquiring an extensive dataset, Naïve Bayes is very helpful and easy to design. K-nearest neighbor produces a dataset, separating it into distinct categories. It predicts new points in the classification. Grounded on the Decision Tree, J48 Classifier uses such facts from the datasets of training, which can be utilized to decide the minor subsection. To measure the precision of the cancer dataset, the Weka tool cab is applied. Its dataset encompasses nine kinds of cancer. A 70% train and 30% test data set split has been utilized to predict the cancer disease. The exactness of Naïve Bayes is 91.81%, whereas the identity of J48 and K-nearest neighbor is respectively 92.98% and 97.07%.
Early diagnosis is essential to enhance the results and survival rates of breast cancer. There are two
strategies for early detection of breast cancer, including screening and early diagnosis. The settings
with limited resources and poor health organizations where most women are detected in advanced
stages must focus on the early detection programs grounded on the consciousness of early symptoms
and quick recommendations for analysis and treatment. Screening comprises assessing women for
identifying the risks of cancer before the appearance of symptoms. These screening tools include
breast self-exam, clinical breast exam, and mammography. Since it requires significant investment,
the decision to continue with the screening procedure should be made following fundamental breast
health amenities that include efficient detection and appropriate treatment. Early diagnosis involves
delivering apt access to cancer treatment by decreasing the barriers that come before enhancing access
to proper diagnosis services. The purpose is to study the comparison of Naïve Bayes, KNN, and J48
classifier accuracy in predicting the proportion of breast cancer identification in the early phases [
2022 Copyright for this paper by its authors. within the body from the location where it developed. This spread of cells from one part to another is known as metastasis. 1.2.
The data mining technique is expanding extremely speedily in the medical ground because of its accomplishment in classification and prediction processes that aid the experts in decision-making. We are searching for ways to improve the patients' health and decrease the expense of medicine, and data mining assists a lot in this case. A few papers are accessible to predict the disorder, and the most crucial part is that it is merged with the prediction of the existence of the specific cancer disease. Many types of cancers are still unknown. Doctors are sometimes unable to find out the reason behind the disease. For curing the disorder, early detection is needed, and undertaking the prediction research is critical. Here we utilize the open-source data mining tool Weka, which was created at Waikato University, New Zealand. It assists us in predicting the cancer disease precisely and aids in proper decision-making. We have used renowned classification procedures called K-Nearest, J48, and Naïve Bayes. A dataset of breast cancer diagnosed patients is used to show an apt answer. 1.3.
The present paper has seven sections that initiate with the introduction segment and is followed by Related Work, Problem Statement, Data and Classifier Details, Methodology, Results and Analysis, and Conclusion sections.
Scientists are striving hard to reduce the consequences of malignancy. Thus, there are multiple queries regarding predicting the survivability of cancer. Thousands of people pass away due to the most frequent breast cancer. It occurs in humans owing to damaged genes caused because of increasing age. Age activates a combination of factors generating mutations, which, in turn, develops tumors. Thus, early diagnosis is necessary; hence, designing a genetic mutation-based strategy to predict cancer has become essential.
The Clustering procedure and diverse classification procedures were utilized by Dona Sara Jacob
et al. [
Alom et al. [
According to Rashmi et al. [
Breast cancer is the most prevalent form of cancer, where a painless, hard lump is the most
prominent symptom of the breast. Like most tumors, breast cancer can be best treated when diagnosed
early. Early detection of breast cancer raises the number of viable treatment choices and dramatically
increases the probability of clinical success and recovery. About 70% of healthy tumors are reported
with this method, but between 4% and 34% of breast carcinoma cases cannot be diagnosed by
mammography. A number of researches on artificial intelligence, computer learning, and data
processing were reviewed to identify breast cancer. Danacı et al. [
The present study aimed to gain a comparative insight into decision-making processes by the
classification of breast cancer data, such as naive Bayes, naïve Bayes, and J48 decision trees. In this
study, the breast cancer dataset was taken from Kaggle, which Syeda Daraqshn contributed, and was
examined using the Weka tool [
The cancer patients’ data were collected from the Kaggle website created on 28/08/2020 [
The most heard term in Weka is a classification that assists in decision-making. Classification can be divided into two parts: multi-class targets and binary. The latter comprises two kinds of outcomes, whereas the former aims at delivering superior two values. The key objective is to categorize and forecast accurately. Forecasting relies on some associated data that can inform us about further happenings. Prediction develops a connection between the data that people are aware of and the data that people require forecasting in the future. Among several types of classifiers, three different types are utilized in this paper K-Nearest Neighbor, J48 algorithm, and Naïve Bayes. We used Weka 3.9 version and Windows 10 OS.
Naïve Bayes is a straightforward technique regarded as "probabilistic classifiers" [11]. It encompasses an amalgamation of procedures that share standings, on which each aspect is categorized self-reliantly from other presented values. It is executed in the supposition that the impression of a specific value does not rely upon other characteristic values. This particular algorithm is very helpful to acquire a big dataset and extremely easy to develop.
K-Nearest Neighbor (KNN) is a non-parametric and indolent procedure that utilizes data sets and
produces a new dataset, separating it into distinct categories and forecasting new points in
classification [
J48 utilizes a non-proprietary Java incorporation C4.5 procedure. For instance, if we have a dataset that comprises dependent variables and the other encompasses independent variables, the application of the decision tree such as J48 enables us to forecast new records. It acts fine on incessant and distinct also missed out data values. It even provides a choice for cropping trees after generation. The classifier avails of the avaricious process also decreased the fault. The test condition outcome is displayed as a branch, and a class tag is given for every final node. Typically, the root node is the largest decision tree node. Any path is an adjective concept in a decision tree. In two steps, it typically uses the depth-first method. Tree preparation in the top-down technique takes place. The layout is separated repetitively at this point before the data elements belong to the same class plate.
The following figure presents the process of the classification algorithms with the training dataset. A biopsy has to be performed if symptoms characteristics indicate malignancy. Otherwise, there will be no requirement for the biopsy test in the case of benign characteristics. The data mining process involves two sections. First, the classification models were trained with the 70% split train dataset. Finally, prediction using the test dataset has been noted.
In this study, three classification algorithms have been used to compare their performances regarding accuracy in correctly predicting the instances in Weka 3.9 version. The best algorithm has been chosen based on the best accuracy of prediction. The benign and malignant classes have been analyzed based on error rate, accuracy, precision, F-score, sensitivity, and specificity. Whereas sensitivity will indicate the true value, specificity indicates the correct negative cases. A cost-benefit analysis has discussed the confusion matrices for all three cases. The confusion matrices were interpreted in terms of TP, FP, TN, FN (T = True, P = Positive, N = Negative, F = False). 6.1.
Exploration of 569 instances revealed no missing value present for any attribute. There were 212 malignant and 357 benign cases. 6.2.
Naïve Bayes classifiers are non-deterministic in nature that can handle noisy big data. The algorithm is generally faster than the KNN classifier and is based on the hypothesis that all the factors correlate among them a contribution to the classification. In the present scenario, the algorithm correctly predicted 91.81% instances (n = 157) from the test dataset of 171 total instances. The precision of prediction was higher for benign cases compared to malignant tumors. The confusion matrix revealed that 59 instances were predicted correctly out of 65 for malignant cases, whereas 98 instances for benign cases were properly predicted out of 106. The following table provides a detailed, accurate description of the two classes. The root mean square (RMSE) error was 0.29, and the relative absolute error (RAE) was 17.49%.
K-Nearest Neighbor (Lazy IBK) is a non-deterministic classifier that is generally slower for huge data sets and refuses to deal with the noisy dataset. The KNN algorithm for N = 1 predicted 95.91% of instances from the test dataset. The classifier produced an optimum prediction of 97.07% correctly classified instances at N = 4. The precision of prediction was higher for benign cases compared to malignant tumors. The confusion matrix revealed that 63 instances were predicted correctly out of 65 for malignant cases, whereas 103 instances for benign cases were properly predicted out of 106 instances. The following table provides a detailed, accurate description of the two classes. The RMSE was 0.16, and the RAE was equal to 10.10%.
J48 Decision Tree is a deterministic classifier that can deal with a noisy large dataset with high accuracy. The J48 is a depth-first or breadth-first approach technique consisting of roots nodes and internal and leaf nodes. In the present study, the classifier produced an optimum prediction of 92.98% correctly classified instances. The precision of prediction was higher for benign cases compared to malignant tumors. The confusion matrix revealed that 58 instances were predicted correctly out of 65 for malignant cases, whereas 101 instances for benign cases were properly predicted out of 106. The following table provides a detailed, accurate description of the two classes. The RMSE was 0.26, and the RAE was equal to 17.72%. 0.95 0.11
This paper is chiefly grounded on the medical dataset that can forecast Cancer prevalence from the symptoms. In this case, three different classification procedures are used to validate the dataset. For fulfilling this purpose, we utilized the Weka 3.9 tool. The application consists of classification and data exploration. The dataset does not create any viciousness as it does not consist of any personal details. It comprises a few medical info. From the evaluation, we can state that KNN did the most accurate classification for N= 4, which could correctly classify almost 97% of instances. For detecting the confusion matrix, three distinct algorithms are utilized. It comprises details regarding the actual and forecasted classification. There are two stages of this disorder- the one is malignant, and the other one is benign stages. For having a clear perception, tables are needed to be created. It compares the three different classification procedures, namely K-Nearest Neighbor, Naïve Bayes, and J48 algorithms. The contrast table lucidly states which model of classification will be better. All three algorithms function outstandingly; however, in this study, K-Nearest Neighbor has worked more supremely than the other two procedures: the J48 algorithm and Naive Bayes. In the upcoming days, we will strive to revolutionize the technologies and equipment for more significant improvement, expand datasets, and implement distinct preparation, clustering, classification, visualization, and regression. In addition, we will act in advancing the new models’ survivability and predictive capabilities.
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