Expert cancer model using supervised algorithms with a LASSO selection approach

Abstract

One of the most critical issues of the mortality rate in the medical field in current times is breast cancer. Nowadays, a large number of men and women is facing cancer-related deaths due to the lack of early diagnosis systems and proper treatment per year. To tackle the issue, various data mining approaches have been analyzed to build an effective model that helps to identify the different stages of deadly cancers. The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression (henceforth LR), decision tree (henceforth DT), random forest (henceforth RF), Support vector machine (henceforth SVM), and K-nearest neighbor (henceforth KNN). After an appropriate preprocessing of the dataset, least absolute shrinkage and selection operator (LASSO) was used for feature selection (FS) using a 10-fold cross-validation (CV) approach. Employing LASSO with 10-fold cross-validation has been a novel steps introduced in this research. Afterwards, different performance evaluation metrics were measured to show accurate predictions based on the proposed algorithms. The result indicated top accuracy was received from RF classifier, approximately 99.41% with the integration of LASSO. Finally, a comprehensive comparison was carried out on Wisconsin breast cancer (diagnostic) dataset (WBCD) together with some current works containing all features.

A proposed model for diabetes mellitus classification using coyote optimization algorithm and least squares support vector machine

“…K-Fold CV divides data into 𝐾 folds. At each iteration, one-fold (𝐾) is used as test data set while training data set is resided folds (K1) in 𝐾 experiments [51], [52]. In this work, the value of 𝐾 = 10 folds, nine data sets for training and one for testing, then repeat this process ten times until all data has been evaluated.…”

Analysing most efficient deep learning model to detect COVID-19 from computer tomography images

“…The scientific community has agreed on a number of criteria for evaluating the classification system's quality [22]- [24]. The confusion matrix is used to assess the study's success using the following key parameters: true-positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN) Validity metrics such as accuracy, sensitivity/recall, specificity, F1-score, precision/positive predicted value (PPV), negative predicted value (NPV), false-negative rate (FNR), false-positive rate (FPR), false discovery rate (FDR), false omission rate (FOR), and Matthews correlation coefficient (MCC) can be calculated using these parameters [25]- [30].…”

Supervised machine learning based liver disease prediction approach with LASSO feature selection

“…As the name suggests, the algorithm is formed in a tree structure with the root node, branches and leaf nodes that indicate attributes, conditions and outcomes respectively [14]. Entropy as denoted in (1) shows the homogeneity as well as the purity of a dataset, and information gain is the change in an input's entropy, which is usually a reduction [15].…”

“…In recent years, a significant increase of various liver diseases has been observed around the globe. In India, the mortality rate because of the disease is 2.4% of the population [1]. There are more than 100 types of liver diseases among which cirrhosis is diagnosed when the liver cells are damaged and replaced by non-living scar tissues [2].…”