Advancements in Medical Diagnosis through Machine Learning: A Comparative Study of Brain Tumour, Heart Disease, and Breast Cancer Detection (KEY IJP************234)

• Ritu Saini,Dr. Priya Mathur

Abstract

Machine learning has revolutionized the accuracy of diagnosis and treatment of such complex diseases as tumours in the brain, heart disease, and breast cancer. This is through its application in high algorithms that machine learning is processing large-sized datasets by finding patterns that may not be easily detected by human expertise on medical images, patient histories, and clinical data. This paper performs a comparative analysis of recent ML techniques-such as deep learning, feature selection, and ensemble methods-that have proven to improve the accuracy of these diagnostics significantly. Deep learning, in particular for image recognition tasks, has shown to improve the precision of the detection of abnormalities in brain scans or mammograms. One of the primary optimization techniques applied to optimize the prediction model for the heart disease application is feature selection. It focuses on the variables that have most influence on the application. Ensemble methods also improved performance because multiple models were used to minimize errors. Machine Learning (ML) Deep Learning (DL) Brain Tumour Detection Heart Disease Prediction Breast Cancer Detection Image Processing EEG Signal Processing