Colon Cancer Stage Classification Using Decision Trees

M, Bhargavi; Mudunuru, Venkateswara Rao; Veeramachaneni, Sireesha

doi:10.1007/978-981-15-1097-7_50

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…The mean (standard error) of the age of the subjects and Tumour sizes for the entire data are 67.74 (0.08) and 48.14 (0.2) respectively. Detailed data description can be found elsewhere (Bhargavi et al, 2020).…”

Section: Datamentioning

confidence: 99%

Age Dependent Analysis of Colon Cancer Tumours Using Mathematical and Statistical Modelling

Machavaram¹,

Veeramachaneni²

2021

Int J Math, Eng, Manag Sci

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Colon cancer is the third most commonly diagnosed cancer and the second leading cause of cancer death in men and women combined in the United States. In this work, we performed mathematical and statistical modelling of Tumour sizes as a function of age for four different races. Mathematically, based on the behaviour of the data for each race, we partitioned ages of subjects into several intervals. The mathematical function that characterizes the size of the Tumour as a function of age was determined for each age interval. Statistically, using quantile regression, we designed models that are more robust at specific quantiles using Tumour size and age as dependent and predictor variables.

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Section: Datamentioning

confidence: 99%

Age Dependent Analysis of Colon Cancer Tumours Using Mathematical and Statistical Modelling

Machavaram¹,

Veeramachaneni²

2021

Int J Math, Eng, Manag Sci

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“…DT uses supervised learning technology and has a classification (or prediction) function for well-known machine learning techniques in various application domains. Thus, DT results in fast formations and produces an entropy-based decision rule of easy if–then–else interpretations in a variety of fields; it has become a common application technique among classification approaches [ 46 , 47 ]. DT algorithms contain two stages: developing a tree and pruning a tree.…”

Section: Related Workmentioning

confidence: 99%

Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular Disease

Chen

Cheng

Chen

et al. 2020

Entropy

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Since 2001, cardiovascular disease (CVD) has had the second-highest mortality rate, about 15,700 people per year, in Taiwan. It has thus imposed a substantial burden on medical resources. This study was triggered by the following three factors. First, the CVD problem reflects an urgent issue. A high priority has been placed on long-term therapy and prevention to reduce the wastage of medical resources, particularly in developed countries. Second, from the perspective of preventive medicine, popular data-mining methods have been well learned and studied, with excellent performance in medical fields. Thus, identification of the risk factors of CVD using these popular techniques is a prime concern. Third, the Framingham risk score is a core indicator that can be used to establish an effective prediction model to accurately diagnose CVD. Thus, this study proposes an integrated predictive model to organize five notable classifiers: the rough set (RS), decision tree (DT), random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM), with a novel use of the Framingham risk score for attribute selection (i.e., F-attributes first identified in this study) to determine the key features for identifying CVD. Verification experiments were conducted with three evaluation criteria—accuracy, sensitivity, and specificity—based on 1190 instances of a CVD dataset available from a Taiwan teaching hospital and 2019 examples from a public Framingham dataset. Given the empirical results, the SVM showed the best performance in terms of accuracy (99.67%), sensitivity (99.93%), and specificity (99.71%) in all F-attributes in the CVD dataset compared to the other listed classifiers. The RS showed the highest performance in terms of accuracy (85.11%), sensitivity (86.06%), and specificity (85.19%) in most of the F-attributes in the Framingham dataset. The above study results support novel evidence that no classifier or model is suitable for all practical datasets of medical applications. Thus, identifying an appropriate classifier to address specific medical data is important. Significantly, this study is novel in its calculation and identification of the use of key Framingham risk attributes integrated with the DT technique to produce entropy-based decision rules of knowledge sets, which has not been undertaken in previous research. This study conclusively yielded meaningful entropy-based knowledgeable rules in tree structures and contributed to the differentiation of classifiers from the two datasets with three useful research findings and three helpful management implications for subsequent medical research. In particular, these rules provide reasonable solutions to simplify processes of preventive medicine by standardizing the formats and codes used in medical data to address CVD problems. The specificity of these rules is thus significant compared to those of past research.

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