Multi Disease Prediction Using HDO Machine Learning Approach
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Abstract
Several machine learning approaches can do predictive analytics on vast volumes of information in various sectors. Predictive analytics in health care is a challenging task. Still, it may ultimately aid physicians in making timely judgments about the health and handling of patients based on vast amounts of information. Breast cancer, diabetes, and heart-related disorders cause numerous fatalities worldwide, yet most of these decreases are attributable to an absence of appropriate screenings. The lack of remedial substructure and a short doctor-to-population proportion contribute to the issue above. Following WHO recommendations, physicians' ratio to affected persons should be in some range; India's doctor-to-public proportion indicates a doctor scarcity. Heart, cancer, and diabetes-related disorders pose a significant danger to humanity if not detected initially. Thus, early detection and identification of these disorders may save many lives. Using classification methods based on machine learning, the focus of this effort is to anticipate dangerous illnesses. Diabetes, heart disease, and breast cancer are discussed in this study. To make this effort easy and accessible to the general community, a web application for therapeutic tests has been developed that use machine learning to create illness predictions. In this study, a web application is created for illness prediction that employs the notion of machine learning-based forecasts for illnesses such as breast cancer, diabetes, and cardiovascular sickness.
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