Integrating Neural Network and SVM for Early Lifestyle Disease Diagnosis

The average life expectancy has increased by 15 to 20 years in recent decades due to major advancements in public health. Developments in public policy, economic growth, medical diagnoses, and treatment approaches have all contributed to these developments. However, the 21st century's fast urbanization and industrialization have changed food and lifestyle patterns, which has led to an increase in non-communicable diseases (NCDs). The fact that these diseases, which include obesity, type 2 diabetes, hypertension, and several types of cancer, currently cause 63% of deaths worldwide shows how pervasive they are. Poor diet, inactivity, and sedentary work habits are behavioral variables that contribute to lifestyle-related illnesses (LRDs), a subgroup of noncommunicable diseases (NCDs) that include diabetes, stroke, and cardiovascular disease. Early detection and care are essential because these behaviors aggravate metabolic problems and other health issues. Despite being common, these illnesses can be difficult to diagnose because of their complexity and reliance on lifestyle and environmental factors. The requirement for a solid and dependable framework to accurately forecast and control LRDs is addressed in this study. It presents the NNSVM model, a hybrid method that combines feature selection, disease prediction, and practical suggestions for bettering one's lifestyle. The system uses lifestyle data and cutting-edge machine learning techniques to predict diseases like diabetes, depression, and hypertension in their early stages. This novel approach may strengthen efforts to prevent illness and assist people in changing their lifestyles in a way that will improve their health.