Prediction of Cancer Using Logistic Regression, K-Star and J48 algorithm

Maliha, Shanjida Khan; Islam, Tajul; Ghosh, Simanta Kumar; Ahmed, Helal; Mollick, Md. Rafsun Jony; Ema, Romana Rahman

doi:10.1109/eict48899.2019.9068790

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…Then the data was evaluated only with the lazy algorithms, from the error it was possible to determine the high performance of the instancebased algorithms (LW, IBK and KSTAR). While Maliha et al (2019) to predict the causes and appearance of cancer found when using algorithms J-48 and KSTAR that in logistic regression the accuracy is 99,3%; for KSTAR it was 99,5% and J-48 is 99,1 %.…”

Section: Resultsmentioning

confidence: 99%

Adaptability of regression algorithms to protein Plant behavior

et al. 2023

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The behavior of components of protein plant is of vital importance for animals that consume them in their diet. The objective of this research is to evaluate regression algorithms, to determine the behavior of the expressions that best adapt to the procedures of a traditional laboratory and to estimate the chemical components of protein plants, in this sense the MULAN library of java has been used, that contain automatic learning algorithms capable of adapting to dissimilar problems. Three data set were created for each species treated in this study; each of these include the main elements to be evaluate in each experiment, these are delimits by: secondary metabolites, cell wall components and digestibility element for training files one, two and three, respectively; subsequently, they were evaluated through learning supervised and cross-validation of each to determine the best fit by aRMSE (Average Root Mean Square Error). The learning results were compare with previous experiments, where there was a learning variant that contained in a single dataset all the components to be evaluates in a single prediction. The result of the comparison shows that the lazy algorithms based on instances have a better learning behavior than the others evaluate.

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Section: Resultsmentioning

confidence: 99%

Adaptability of regression algorithms to protein Plant behavior

et al. 2023

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“…This study aimed to construct an intelligent predictive model via leveraging the selected ML algorithms to predict the BC and effectively differentiate between positive and negative BC cases. We trained six well-known classification algorithms, including AB, LR, MLP NB, J-48, and RF, according to the top related parame- (34,35), lesion biopsy (21), blood tests (36), etc. However, we considered more cost-benefit and available data with the minor intervention features for our prediction models.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, research samples can be classified with the highest performance and most discriminative capability. The capability of J-48 decision tree algorithms allow to embed the continuous variables for DM, use the most technical features to prevent overfitting, and adjust the decision size with confidence factors (35,36).…”

Section: Model Development and Assessmentmentioning

confidence: 99%

Comparing Data Mining Algorithms for Breast Cancer Diagnosis

et al. 2022

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Background: Early screening and diagnosis of breast cancer (BC) is critical for improving the quality of care and reducing the mortality rate. Objectives: This study aimed to construct and compare the performance of several machine learning (ML) algorithms in predicting BC. Methods: This descriptive and applied study included 1,052 samples (442 BC and 710 non-BC) with 30 features related to positive and negative BC diagnoses. The data mining (DM) process was implemented using the selected algorithm, including J-48 and random forest (RF) decision tree (DT), multilayer perceptron (MLP), Naïve Bayes (NB), Adaboost (AB), and logistics regression (LR) classifier. Then, we obtained the best algorithm by comparing their performances using the confusion matrix and area under the receiver operator characteristics (ROC) curve (AUC). Finally, we adopted the best model for BC prognosis. Results: The results of evaluating various DM algorithms revealed that the J-48 DT algorithm had the best performance (AUC = 0.922), followed by the AB, MLP, LR, and RF algorithms (AUC: 0.899, 0819, 0.716, and 0.703, respectively). Also, the NB algorithm achieved the lowest performance in this regard (AUC = 0.669). Conclusions: The ML presents a reasonable level of accuracy for an early diagnosis and screening of breast malignancies. Also, the empirical results showed that the J-48 DT algorithm yielded higher performance than other classifiers.

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“…It differs from other instance based learners in that it uses an entropy based distance function. [ 56 57 ]…”

Section: Methodsmentioning

confidence: 99%

Performance evaluation of selected machine learning algorithms for COVID-19 prediction using routine clinical data: With versus Without CT scan features

Shanbehzadeh

Kazemi-Arpanahi

Orooji

et al. 2021

J Edu Health Promot

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BACKGROUND: Given coronavirus disease (COVID-19's) unknown nature, diagnosis, and treatment is very complex up to the present time. Thus, it is essential to have a framework for an early prediction of the disease. In this regard, machines learning (ML) could be crucial to extract concealed patterns from mining of huge raw datasets then it establishes high-quality predictive models. At this juncture, we aimed to apply different ML techniques to develop clinical predictive models and select the best performance of them. MATERIALS AND METHODS: The dataset of Ayatollah Talleghani hospital, COVID-19 focal center affiliated to Abadan University of Medical Sciences have been taken into consideration. The dataset used in this study consists of 501 case records with two classes (COVID-19 and non COVID-19) and 32 columns for the diagnostic features. ML algorithms such as Naïve Bayesian, Bayesian Net, random forest (RF), multilayer perceptron, K-star, C4.5, and support vector machine were developed. Then, the recital of selected ML models was assessed by the comparison of some performance indices such as accuracy, sensitivity, specificity, precision, F-score, and receiver operating characteristic (ROC). RESULTS: The experimental results indicate that RF algorithm with the accuracy of 92.42%, specificity of 75.70%, precision of 92.30%, sensitivity of 92.40%, F-measure of 92.00%, and ROC of 97.15% has the best capability for COVID-19 diagnosis and screening. CONCLUSION: The empirical results reveal that RF model yielded higher performance as compared to other six classification models. It is promising to the implementation of RF model in the health-care settings to increase the accuracy and speed of disease diagnosis for primary prevention, screening, surveillance, and early treatment.

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Prediction of Cancer Using Logistic Regression, K-Star and J48 algorithm

Cited by 3 publications

References 4 publications

Adaptability of regression algorithms to protein Plant behavior

Adaptability of regression algorithms to protein Plant behavior

Comparing Data Mining Algorithms for Breast Cancer Diagnosis

Performance evaluation of selected machine learning algorithms for COVID-19 prediction using routine clinical data: With versus Without CT scan features

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