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Analysis Study Research Paper | Computer Science & Engineering | India | Volume 13 Issue 7, July 2024 | Rating: 4.1 / 10
Forecasting Health: Machine Learning Approaches to Disease Prediction
Nandana Santhosh | Prayag Tushar | Rohan Gilroy Gomez | Devanarayanan V
Abstract: Machine learning (ML) is profoundly impacting the healthcare sector in the present world, offering transformative benefits across various aspects of patient care and healthcare delivery. Disease prediction plays a pivotal role in healthcare by enabling proactive interventions and personalized treatment strategies. By leveraging advanced data analytics and machine learning techniques, healthcare providers can identify individuals at high risk of developing specific diseases even before symptoms manifest. Early detection facilitated by disease prediction models allows for timely initiation of preventive measures and treatment interventions, leading to improved patient outcomes and reduced healthcare costs. Disease prediction not only enhances individual patient care but also supports public health initiatives, ultimately contributing to better health outcomes and improved healthcare delivery. Machine learning algorithms enable more accurate diagnosis and prognosis by analyzing patient data, leading to earlier disease detection and personalized treatment plans. Predictive analytics powered by Machine learning identify disease trends and risk factors, facilitating preventive interventions and public health initiatives. Machine learning accelerates drug discovery and development processes, optimizes healthcare operations, and enhances remote monitoring and telemedicine capabilities. Additionally, Machine learning-based medical imaging analysis improves diagnostic accuracy and workflow efficiency. Overall, Machine learning is revolutionizing healthcare by improving patient outcomes, enhancing population health, and transforming the delivery of healthcare services on a global scale.
Keywords: Disease prediction, Machine Learning, SVM Classifier, Na?ve Bayes Classifier, Random Forest Classifier, testing, training
Edition: Volume 13 Issue 7, July 2024,
Pages: 1099 - 1104