Impact on Human Mental Behavior after Retirement Using Supervised Machine Learning based on Decision tree and Neural Network

Some current studies have proven that retirement would affect the psychology of the retired person, especially after the sudden change in his life and the complete change in his lifestyle. Most retired people find themselves without a specific daily activity or daily schedule that would help them spend their day, and this subsequently affects their mental care. The objective of this paper is to study hypotheses that present the different cases resulting from the retired persons based on deduction rules. We developed a predictive model to determine whether a retired person may be depressed based on supervised machine learning systems. This paper describes Supervised Machine Learning (ML) classification techniques, compares two
supervised learning algorithms as well as determines the most efficient classification algorithm based on the data set, the number of instances and variables (features). The process will be carried out through the use of a decision tree (J48) and Neural Networks (Multi-layer Perceptron), which produce a binary classification and predictive model that can handle categorical data and is straightforward to comprehend and interpret. A
random sample of people is chosen for the experiment and fed into our prediction model. Mention the overall number of study participants; time frame for the study; the region or category examined. The result shows that retirement may not affect mental changes on the retired person such as stress, depression, mood swing, if other factors are satisfied such as his relationship with his family, leisure activities, financial status, etc. A
comparison of performance between J48 and Multi-layer Perceptron classifiers is performed, and accuracy is obtained.