AI-Driven Data Science Approaches for Enhancing Personalized Medicine and Clinical Decision-Making

Abstract

Diabetes and leukemia are two separate medical conditions, but research has found that people with type 2 diabetes have a 20 percent most chance of developing blood malignancies such as acute leukemia, showing a link between the two. Early identification of these disorders by studying biological datasets is critical for providing prognostic information. However, the class imbalance and high dimensionality problems in Machine Learning (ML)-based techniques have often degraded effective analysis of clinical and genomic datasets for disease detection. This paper focuses on developing an efficient clinical decision support system using advanced metaheuristic and ML algorithms to solve class imbalance and high dimensionality problems. The first stage of the proposed approach utilizes an optional data augmentation and another pre-processing method for outlier detection and removal using Modified Z-Score (MZS) based on the Median Absolute Deviation (MAD) metric. Then, the optimal features/genes are selected using a hybrid Firefly Pearson’s Correlation Coefficient (FPCC)-based Feature/Gene Selection method to reduce the higher feature dimensionality problem. Once the features/genes are selected, the proposed Ladybug Beetle Optimized Universum Learning-based Twin Boosted Adaptive Support Vector Machine (LBO-ULTBASVM) classifier detects the disease with reduced model complexity and error rates. LBO-ULTBASVM is developed by improving the Twin Support Vector Machine (TSVM) classifier by integrating the Universum Learning, Ladybug Beetle Optimization (LBO), and XGBoost for solving the class imbalance problem, reducing training time and improving disease accuracy. Experiments are conducted using PIMA Indians Diabetes and GSE9476 Leukemia datasets and the outcomes indicated that the LBO-ULTBASVM-based model increases the diabetes and leukemia detection accuracy with reduced model complexity and processing time.

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