A hybrid model: PNM for improving prediction capability of classifier

In recent years, the COVID-19 and its variant are more dangerous for people with some health complexity, such as breast cancer, diabetes, and heart disease. Diagnosis at the early stage of these diseases may save people from deadly diseases and infections. However, often these diseases are diagnosed very late. Classification algorithms are in use for the diagnosis of several diseases. Classification result significance is a major issue in many real-time domains which deal with class imbalance data. Class imbalance data is one of the primary challenges for the performance of classification models. Most sensitive and essential real-time domain data are class imbalances, resulting in poor classification performance. Due to the dominancy of the major class, the classification of the minor class may show high accuracy. Misclassification can not be accepted in medical cases, which can cost human life as well. The paper presents a hybrid model named PNM to address the class imbalance issue and thus improve the performance of the classifiers. This proposed sampling model integrates Principal Component Analysis and the Near-Miss sampling method. Experimental results show the efficiency of the PNM model for the class imbalance data. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.