The pandemic has shown us that it is quite important to keep track record our health digitally. And at the same time, it also showed us the great potential of Instruments like wearable observing gadgets, video conferences, and even talk bots driven by artificial intelligence (AI) can provide good care from remotely. Real time data collected from different health care devices of cases across globe played an important role in combatting the virus and also help in tracking its progress. The evolution of biomedical imaging techniques, incorporated sensors, and machine learning (ML) in recent years has led in various health benefits. Medical care and biomedical sciences have become information science fields, with a solid requirement for refined information mining techniques to remove the information from the accessible data. Biomedical information contains a few difficulties in information investigation, including high dimensionality, class irregularity, and low quantities of tests. AI is a subfield of AI and computer science which centric the utilization of information and calculations to impersonate the way that people learn, steadily further developing its accuracy. ML is an essential element of the rapidly growing area of information science. Calculations are created using measurable procedures to make characterizations or forecasts, exposing vital experiences inside information mining operations. In this chapter, we explain and compare the different algorithms of ML which could be helpful in detecting different disease at earlier stage. We summarize the algorithms and different steps involved in ML to extract information for betterment of the society which is already exposed to the world of data.

Invasive cancer is the biggest cause of death worldwide, especially among women. Early cancer detection is vital to health. Early identification of breast cancer improves prognosis and survival odds by allowing for timely clinical therapy. For accurate cancer prediction, machine learning requires quick analytics and feature extraction. Cloud-based machine learning is vital for illness diagnosis in rural areas with few medical facilities. In this research, random forests, logistic regression, decision trees, and SVM are employed, and the authors assess the performance of various algorithms using confusion measures and AUROC to choose the best machine learning model for breast cancer prediction. Precision, recall, accuracy, and specificity are used to calculate results. Confusion matrix is based on predicted cases. The ML model's performance is evaluated. For simulation, the authors used the Wisconsin Dataset of Breast Cancer (WDBC). Through experiments, it can be seen that the SVM model reached 98.24% accuracy with an AUC of 0.993, while the logistic regression achieved 94.54% accuracy with an AUC of 0.998.