Personalized Health Assessment and Recommendations Through Iot and Mlp Classifier Algorithms

Procuring a healthy lifestyle involves a holistic approach of personalized dietary and exercise recommendations dependent on individual health statuses. In this study, we present a new paradigm for examining individual health statuses for easy self-assessment without specialist help. The heart is a full kit of assessing instruments that can align critical climacterics of body temperature, pulse rate, blood oxygen level, and body max index that could be run with minor medic assistance. The research abides a dataset obtained through a broad scope of volunteers aged 17 to 24 including both males and females. Vital signs such as SpO2, BPM, temperature, and BMI are mediated utilizing incorporated Internet of Things units. The dataset is then cautiously preprocessed and balanced using machine learning algorithms before examination. The basis of this model is a two-tier state classifier system that designs autonomous dietary and exercise responsibilities varying from examined health clots. It is exploited for adulthood healthcare systems across multiple machines learning techniques, including Decision Tree, KNN, and some classifiers with the MLP classifier being the exemplary worthy model. The MLP classifier demonstrates unbelievable outcomes through approximately 86% accuracy when the trainings and testing datasets are 70:30 ratios apart