Predicting Wealth Index Using an Ensemble Model of Random Forest and Multilayer Perceptron

Wealth inequality remains a significant challenge in Kenya, exacerbated by the limitations of traditional wealth measurement methods. This study develops and evaluates an ensemble wealth index model combining Random Forest (RF) and Multilayer Perceptron (MLP) algorithms to improve prediction accuracy using socio-economic data from the 2019 Kenya Population and Housing Census (KPHC). The models were assessed using performance metrics such as accuracy, precision, sensitivity, specificity and ROC-AUC. The Random Forest model, configured with 500 trees and a split variable count of 2, achieved 34.3% accuracy, 58.54% balanced accuracy, 65.98% out-of-bag error and performed best on the ”Poorest” class with a 13.7% class error. It further recorded 41.13% precision, 34.27% recall, 83.17% specificity, and a 67.31% AUC. The MLP model, using sigmoid activation in hidden layers and softmax in the output layer, achieved 33.4% accuracy, 57.7% balanced accuracy, 30.6% precision, 32.54% recall, 82.86% specificity and AUC of 69.12%. The RF-MLP ensemble model outperformed the individual models with a 34.4% accuracy, 37.42% precision, 34.05% recall, 83.2% specificity and AUC of 68.55%. Despite modest overall accuracies, the ensemble model showed enhanced balanced accuracy and specificity, particularly in extreme wealth categories. However, classification of middle and poor wealth levels remains challenging due to feature overlap and class imbalance.