MRI Based Health Detection Using Machine Learning Techniques

Health has a significant influence on one's wellbeing and quality of life. By assisting medical practitioners, suggesting possible therapies, and allowing early illness identification, machine learning techniques have the potential to completely transform the healthcare sector. This research assesses many machine learning approaches, such as Decision Tree classifier, Random Forest classifier, Logistic Regression, and Support Vector Machine, for identifying heart health concerns. It also offers a Convolutional Neural Network (CNN) model for brain tumor diagnosis. CNNs may detect tiny anomalies that may escape human inspection because of their expertise in processing medical pictures, such as MRI scans. The suggested 11-layer CNN model for cancer diagnosis has a 97.7% accuracy rate, a 96.9% recall rate, and a 1.36% false positive rate. Furthermore, by evaluating large amounts of patient data to pinpoint those who are at risk, machine learning algorithms also significantly contribute to the prediction of cardiac disease.