Multiple Disease Prediction Using Machine Learning

The early and accurate prediction of multiple diseases is crucial for timely intervention and effective treatment. Machine Learning (ML) techniques have shown significant potential in healthcare by analyzing large datasets to identify patterns and predict diseases with high accuracy. There are humongous amounts of data that are associated with the diseases that face the healthcare industry nowadays. Nevertheless, most of the data that exists is wasted in the sense of creating new meaningful insights for decision-making and choice. The scope of the project is to use the predictive power of an SVM algorithm in making attempts to utilize the patterns surrounding the patient's lifestyles and hence make predictions regarding a human being's vulnerability to lifestyle diseases. The model thus depicts a cheaper option to the traditional diagnosis testing either genetic or DNA screening that assesses lifestyle factors which cause disease. The parameters like poor diet, excessive energy consumption, and lack of physical activity are responsible to a large extent for preventable lifestyle diseases if intervention is made in time. The application of SVM and other machine learning algorithms in this research will try to develop a model that might make lifestyle-based predictions and thus act as an inexpensive tool for predicting genetic disorders without undergoing costly tests.