Data-Driven Disease Prediction and Lifestyle Monitoring System

The usage of machine learning techniques has been instrumental in the healthcare industry to a great extent for the past few years. The rise in disease rates highlights the urgent need for quick medical attention. Hospitals often struggle with efficiently sorting and prioritizing patients due to the overwhelming influx of individuals seeking care. The time-consuming manual review of patient records to identify their specific conditions has become a bottleneck in healthcare delivery. To tackle this challenge, artificial intelligence (AI) has emerged as a crucial tool in the healthcare sector. AI utilizes data from diagnostic procedures not only to deepen our understanding of diseases but also to significantly enhance treatment success rates. The proposed healthcare system, we employ various machine learning algorithms like logistic regression, linear regression, Support Vector Machine, and K Nearest Neighbors to offer precise and personalized predictions and insights. The Support Vector Classifier (SVC) achieves 87.2% accuracy for diabetes prediction, while the random forest model excels in breast cancer prediction with 95.98% accuracy. For heart disease, Support Vector Machine (SVM) attains 86.38% accuracy, and linear regression achieves 96.46% accuracy for caloric expenditure prediction, illustrating machine learning's versatility in healthcare. The research work serves as a user-friendly platform for disease prediction and health monitoring, aiming to empower individuals to actively manage their health. Ultimately, the integration of AI-driven machine learning models into healthcare applications represents a significant advancement. It streamlines patient prioritization and equips individuals with the knowledge to make informed health decisions. Data mining techniques coupled with machine learning techniques can further increase the accuracy of disease prediction.