Multiple Disease Prognostication Based On Symptoms Using Machine Learning Techniques

Abstract

Disease Prediction system that uses Machine Learning forecasts the ailments on the basis of the data pertaining to the symptoms entered by the user and provides trustworthy findings based on that data. If the patient isn’t in any danger and the user merely wants to know what kind of ailment he or she has had. It is a system that gives the user suggestions and methods on how to keep their health system in good shape, as well as a way to find out if they have a sickness utilizing this forecast. Due to a diversity of diseases and a lower doctor-patient ratio, the use of particular disease prediction technologies as well as concerns about health has risen. We are focusing on offering customers with an instant and accurate disease prognosis based on the symptoms they enter, as well as the severity of the condition projected. It will provide the best algorithm and doctor consultation. Different machine learning algorithms are employed to forecast illnesses, ensuring speedy and reliable predictions.

Machine Learning for the Diagnosis and Prognosis of Chronic Illnesses

“…Surveys of patients, analyses of text, and data collected by wearable sensors all contribute to the data set [19]. Issues of Ethics and Personal Data Protection A new field of study is developing to address ethical issues with transparency, algorithmic bias, and data privacy [20]. A rising concern is checking that illness prediction models don't break any ethical rules [21].…”

A Machine Learning Approach for Multiple Disease Prediction

“…[8]Noreen Fatima proposed work on the lung disease the data mining techniques and machine learning techniques that can predict cancer effectively on the large health records and described the study previous existing models. [9]Ch. Shravya, K. Pravallika, Shaik Subhani presented the work on lung disease prediction using Supervised machine learning techniques on the dataset and also analyzed the results with used the dimensionality reduction and explained in a wellmannered way.…”

Multi Disease Detection Using Machine Learning

“…Further, they want to enhance their work using different feature selection models. Finally, in the study [15], they are predicting the common cold, malaria, and Typhoid using Multinomial Naive Bayes, Logistic Regression, and Decision tree and the obtained accuracies are 92%, 98%, and 97% respectively. We have chosen 5 models and an ensemble voting classifier, which is very efficient.…”