Machine Learning of Medical Applications Involving Complicated Proteins and Genetic Measurements

Abstract

Motivations. Breast cancer is the second greatest cause of cancer mortality among women, according to the World Health Organization (WHO), and one of the most frequent illnesses among all women today. The influence is not confined to industrialized nations but also includes emerging countries since the authors believe that increased urbanization and adoption of Western lifestyles will lead to a rise in illness prevalence. Problem Statement. The breast cancer has become one of the deadliest diseases that women are presently facing. However, the causes of this disease are numerous and cannot be properly established. However, there is a huge difficulty in not accurately recognizing breast cancer in its early stages or prolonging the detection process. Methodology. In this research, machine learning is a field of artificial intelligence that employs a variety of probabilistic, optimization, and statistical approaches to enable computers to learn from past data and find and recognize patterns from large or complicated groups. The advantage is particularly well suited to medical applications, particularly those involving complicated proteins and genetic measurements. Result and Implications. However, when using the PCA method to reduce the features, the detection accuracy dropped to 89.9%. IG-ANFIS gave us detection accuracy (98.24%) by reducing the number of variables using the "information gain" method. While the ANFIS algorithm had a detection accuracy of 59.9% without utilizing features, J48, which is one of the decision tree approaches, had a detection accuracy of 92.86% without using features extraction methods. When applying PCA techniques to minimize features, the detection accuracy was lowered to the same way (91.1%) as the Naive Bayes detection algorithm (96.4%).