A recommender system with multi-objective hybrid Harris Hawk optimization for feature selection and disease diagnosis
Madhusree Kuanr, Puspanjali Mohapatra · Healthcare Analytics · 2025
This study proposes a health recommender system to analyze health risk and disease prediction by identifying the most responsible disease-causing factors using a hybrid Genetic–Harris Hawk optimization multi-objective feature selection approach. The proposed recommender system uses the Tree-based Pipeline Optimization Tool (TPOT) automated machine learning model to recommend the most suitable machine learning prediction model with the best classifier in terms of classification accuracy for a disease with the selected features. It also recommends the top three disease-causing features for a particular disease that can be utilized to analyze a person’s health risk. The proposed system has also been compared with the competing prediction approaches using Principal Component Analysis (PCA), Singular Vector Decomposition (SVD), and Autoencoders. We show that the proposed system outperforms competing approaches in terms of classification accuracy. • Use multi-objective feature selection to optimize important criteria and a subset of features from a high-dimensional dataset. • Use Harris Hawk optimization for a smooth transition between local and global searches. • Use automatic machine learning to automate the iterative procedure in developing machine learning models. • Use tree-based pipeline optimization to combine the Scikit-learn machine learning with a genetic programming stochastic global search technique. • Use Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to seek the most suitable solution considering the ideal and nadir distances.