An Intelli BPS: An Intelligent Biopsychosocial Parameters-Based Machine Learning Model to Predict Hypertension

The biopsychosocial parameters of an individual are major influencing factors behind the good health or illness of a person. In recent times, these factors are extensively under study by doctors in the medical field to diagnose and treat different types of diseases. An individual’s blood pressure (BP) might be elevated as a result of physical, psychological, or emotional trauma. If the systolic blood pressure (BP) consistently exceeds 140 mmHg and the diastolic BP consistently exceeds 90 mmHg in repeated measurements, it is classified as high blood pressure (HBP) or hypertension. The biological parameters such as aging, cholesterol, and obesity, psychological parameters such as anger, stress, and depression, and emotional parameters such as ego, jealousy, the family background, and the living environment play a major role in one’s life and impacting the quality of life. In this paper, we have proposed an Intelligent Biopsychosocial Parameters-based Machine Learning (ML) Model to Predict Hypertension. The proposed model considers biological parameters obesity level, cholesterol level, and age (OCA), psychological parameters anger level, anxiety level (AA) of a person, and social parameter of individual socioeconomic status (SES). The proposed model works in three stages: at stage 1, the model calculates the absolute impact of the biopsychosocial input parameter on BP using Pearson Correlation Coefficient (PCC); at stage 2, the model is trained using linear regression analysis to design a threshold function Hypertension Identification Function (HIF); and at stage 3, the HIF is utilized to predict an individual’s likelihood of being afflicted with hypertension.