Performance Analysis of Ovarian Cancer Detection and Classification for Microarray Gene Data

Abstract

The most common gynecologic cancer, behind cervical and uterine, is ovarian cancer. Ovarian cancer is a severe concern for women. Abnormal cells form and spread throughout the body. Ovarian cancer microarray data can diagnose and prognosis. Typically, ovarian cancer microarray data contains tens of thousands of genes. In order to reduce computational complexity, selecting the most critical genes or attributes in the entire dataset is necessary. Because microarray datasets have limited samples and many characteristics, classifier detection lags. So, dimensionality reduction measures are essential to protect disease classification genes. In this research, initially the ANOVA method is used for gene selection and then two clustering-based and three transform-based feature extraction methods, namely, Fuzzy C Means, Softmax Discriminant Algorithm (SDA), Hilbert Transform, Fast Fourier Transform (FFT), and Discrete Cosine Transform (DCT), respectively, are used to select relevant genes further. Six classifiers further classify the features as normal and abnormal. The NLR classifier gives the highest accuracy for SDA features at 92%, and KNN gives the lowest accuracy of 55% for SDA, Hilbert, and DCT features. With correlation distance feature selection, the NLR classifier attains the lowest accuracy of 53%, and the highest accuracy of 88% is obtained by the GMM classifier.

Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data

“…It aims to reduce the dimensions of the input data by eliminating the most significant features, thereby simplifying further analysis. By applying the DCT method [21], the input vector and its components are orthogonalized, resulting in a reduction in complexity. This method extracts features by selecting coefficients, which is a crucial step with a significant impact on computation efficiency [22,23].…”

Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI

“…Kalaiyarasi et al introduced a state-of-the-art system for performance analysis of machine-learning models using the microarray gene data of ovarian cancer [31]. The authors utilized various techniques, such as discrete cosine transform (DCT), SDA, Hilbert transformation, fast Fourier transform (FFT), and fuzzy C-means cluster (FCM) for feature selection.…”

“…The authors utilized several models in [31] for ovarian cancer detection and ran experiments with and without selective features. Despite good results from the study, K-fold cross-validation was not carried out, thereby leaving doubts about the robustness and generalizability of the proposed approach.…”