Artificial intelligence based medical decision support system for early and accurate breast cancer prediction. (January 2023) - Singh, Law Kumar; Khanna, Munish; Singh, Rekha
Highlights: A soft-computing and machine learning based medical decision support system is proposed for breast cancer disease classification. Two soft-computing algorithms eagle strategy optimization (ESO) algorithm and the gravitational search optimization (GSO) algorithm are employed for the first time for this problem. A novel intelligent classification approach based on these two algorithms for breast cancer infection is also proposed. 12 experiments have been performed using three soft-computing algorithms(for feature selection) and 6 state-of-the-art machine learning classifier(for classification).WDBC dataset is used for the method evaluation. We have been able to attain astonishing results with accuracy up to 98.9578%, sensitivity up to 0.9696, specificity up to 1.000, precision 1.000, F1- score up to 0.9696, and an AUC 0.9980(close to maximum i.e., 1.0000). Abstract: Feature selection, which picks the optimal subset of characteristics related to the target data by deleting unnecessary data, is one of the most important aspects of the machine learning area. A major part of big data preprocessing is feature selection (reduction). There are 2 n alternative feature subsets for every n features, making it difficult to choose the best set of features from a dataset using typical feature selection techniques. Consequently, the present study proposes and suggests a unique feature selection method based on the Eagle Strategy(ESO) Optimization, Gravitational Search Optimization (GSO) algorithm, and their hybrid algorithm. We chose this infection as our subject of investigation since the number of women with breast cancer is increasing rapidly on a global scale. After lung cancer, which affects more women than any other kind of cancer, breast cancer is the second leading cause of cancer mortality. The goal of this study is to categorize breast cancer into two groups using the benchmark feature set (Wisconsin Diagnostic Breast Cancer (WDBC)) and to choose the fewest features (feature selection) to achieve maximum accuracy. This work also provides a hybrid technique for finding important features that combines two algorithms, ESO and the GSO algorithm, while reducing insignificant characteristics (features) and complexity. Soft computing technologies and machine learning algorithms provide a framework for prognostic research by classifying data instances as relevant or irrelevant depending on cancer severity. Thus, this work presented a new approach for classifying breast cancer tumors. In this research, we coupled soft computing methodologies—our implemented algorithms are applied for the first time to this problem—with artificial intelligence-based machine learning strategies to create a prediction model. The efficacy of our suggested technique was evaluated using WDBC breast cancer data sets, and the findings show that our proposed hybrid algorithm performs very well in breast cancer classification. We have been able to attain astonishing results with accuracy up to 98.9578%, sensitivity up to 0.9705, specificity up to 1.000, precision up to 1.000, F1-score up to 0.9696, and an AUC up to 0.9980 (close to maximum, i.e., 1.0000). Our study's goal is to incorporate our findings into a valid clinical prediction system, allowing visual science specialists to make more accurate and effective judgments in the future. Furthermore, our suggested technology might be used to detect a wide range of diseases.