Human Multi-disease Detection Using Machine Learning Techniques: SVM and Logistic Regression

As different diseases are more typical, effective and precise detection techniques are required to guarantee prompt intervention and treatment.The diagnosis and start of treatment are frequently delayed by the high time and resource requirements of traditional diagnostic techniques.We suggest a Multi-Disease Detection System that makes use of machine learning methods in order to overcome this difficulty.Our method makes use of a vast dataset with 130 symptoms that forecasts 41 different diseases.Our system can successfully diagnose a variety of diseases by utilizing techniques such as Support vector machines (SVMs), random forest models, decision trees, logistic regression, and artificial neural networks (ANNs).After a thorough assessment on real-world datasets, our system outperforms current methods with excellent accuracy, sensitivity, and specificity across a variety of illnesses.Our Multi-Disease Detection System with the capacity to significantly enhance improved patient outcomes and reduced medical expenses by facilitating early detection and intervention.