Smart Health Prediction System using Machine Learning for Physiological Data
Ajinkya Pradhan, Shantanu, Ahmad Pathan, Sameer Patil, Dr. Sagar Tambe
Health Prediction, Decision Trees, KNN, Machine Learning, accuracy, efficiency, training, predictions, symptoms
The use of clinical healthcare programs has seen a large boom in attention in recent years. The development of an ailment tracking device that offers flexibility and mobility to track sufferers' fitness situations may be done the usage of a gadget, ideally an online system. The machine indicates the sickness to the consumer while the parameters' evaluation fee reaches a specific outcome. Massive volumes of facts are accrued by the healthcare region, some of that is hid facts that may be used to manual selections. In the healthcare structures, there is a lot of facts. Nonetheless, there are not many efficient analytic gear available to find traits and hidden links inside the statistics. For acquiring the intended outcomes and making defensible judgements based on records, several sophisticated data analysis methods are employed. In the present investigation, a health forecasting system (DPS) has been created using the Naive Bayes and decision Tree algorithms to predict the likelihood of illness in human bodies. The set of rules makes predictions the usage of 15 scientific characteristics, which include age, sex, blood strain, cholesterol, and weight problems. The DPS estimates the scenario whether that a patient gets a sickness or not and which one. It makes viable critical data. In this regard, it's crucial to recognize connections between trends and health factors associated to cardiovascular disease.
Article Details
Unique Paper ID: 160416

Publication Volume & Issue: Volume 10, Issue 1

Page(s): 445 - 456
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