Subset Feature Selection from Medical Data
In today’s data-driven world, the utilization of cutting-edge technologies such as Artificial Intelligence (AI) and metaheuristic algorithms in feature selection has emerged as a pivotal breakthrough in data analytics. These algorithms play a transformative role in the effective subset feature selection process, selecting through vast datasets to identify the most relevant features and attributes. This is particularly crucial when dealing with authentic datasets, and nowhere is this significance more pronounced than in the domain of medical data analysis and disease diagnosis. AI and metaheuristic algorithms have the power to uncover subtle patterns and correlations within complex medical datasets, enabling healthcare professionals to make more accurate and timely diagnoses. By implementing these algorithms, medical researchers can bond the full potential of their data, ultimately improving patient care and saving lives. Looking ahead, the integration of AI and metaheuristics in feature selection remains a cornerstone in the pursuit of precision and efficacy across various domains, with its impact resonating not only in the healthcare industry but also extending its influence to broader applications.
Dr. Nadimi and his collaborators have dedicated their research efforts to the exploration of metaheuristic algorithms for subset feature selection. Their primary focus has been on the selection of effective features while maintaining low costs. Their work is distinguished by its reliance on authentic and real datasets, which has significantly enhanced the quality and relevance of their research. Their contributions span a broad spectrum, encompassing collecting and constructing authentic medical datasets, tackling critical data-related challenges, and the strategic selection of vital features from extensive medical data. Dr. Nadimi and his collaborators have used their proposed and prominent metaheuristic algorithms, in conjunction with prominent metaheuristic techniques, to address the task of feature selection. These advanced algorithms have enabled them to effectively eliminate redundant and irrelevant features, ensuring that the selected features are not only relevant but also essential for the task at hand. This meticulous approach exemplifies their dedication to optimizing the utility of medical data and elevating the precision of disease diagnosis. These studies reflect their commitment to advancing healthcare through data-driven insights and their noteworthy achievements in developing low-cost models for diagnosing diseases such as coronary artery disease, diabetes, leukemia, prostate, and colon. Their contributions have left an indelible mark in medical feature selection and disease diagnosis.
Related Papers
- An improved binary quantum-based avian navigation optimizer algorithm to select effective feature subset from medical data: A COVID-19 case study (2023)
- Binary starling murmuration optimizer algorithm to select effective features from medical data (2023)
- Enhanced whale optimization algorithm for medical feature selection: A COVID-19 case study (2022)
- Binary approaches of quantum-based avian navigation optimizer to select effective features from high-dimensional medical data (2022)
- Binary Aquila optimizer for selecting effective features from medical data: A COVID-19 case study (2022)
- Boosting atomic orbit search using dynamic-based learning for feature selection (2021)
- B-MFO: A binary moth-flame optimization for feature selection from medical datasets (2021)
- Binary sine cosine algorithms for feature selection from medical data (2019)
- A binary metaheuristic algorithm for wrapper feature selection (2019)
- Comparative analysis of transfer function-based binary Metaheuristic algorithms for feature selection (2018)
- Feature selection based on whale optimization algorithm for diseases diagnosis (2016)
- Presentation of hybrid genetic algorithm for effective feature selection (2015)
- A low cost model for diagnosing coronary artery disease based on effective features (2015)
