Deep Learning-Based Ensemble Model for Predicting the Lifestyle Status of a Person

Our lifestyle heavily influences the decisions we make in life. A healthy lifestyle can greatly enhance one's ability to achieve one's goals and aspirations sustainably. Unfortunately, many individuals in the modern era tend to overlook the importance of this attribute, missing out on a tremendous investment in their overall well-being. Thus, mental, and physical health, as well as related attributes, play a pivotal role in determining the value of our lives. Therefore, everyone should have the opportunity to evaluate their lifestyle status. After knowing the status of their own lifestyle they can rearrange their life in a positive direction. As a solution to this issue, the study's main objective is to predict the current status of the lifestyle using deep learning algorithms. In the context of assessing lifestyle, the analysis involves a rich dataset comprising 22 distinct features drawn from an extensive dataset of over 15,000 data points. After pre-processing, to tackle the inherent complexity and variability of this dataset, our research employed deep learning models, specifically Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and SVM (Support Vector Machine). After taking the individual algorithm results, to further enhance the accuracy, precision, recall, and F-measure while reducing error rates, these models were ingeniously combined in an ensemble approach. Leveraging the weighted average method, this ensemble model surpassed the individual models, yielding superior results that could achieve 94.51% accuracy. Moreover, this ensemble strategy contributed significantly to mitigating overfitting and bias, making it a robust and reliable tool for lifestyle assessment.