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Disease Forecast Method using Decision Tree Classifier
¹ Professor, Computer Science, Panipat Institute Of Engineering & Technology, Haryana, India. ²³⁴ Student, Computer Science, Panipat Institute Of Engineering & Technology, Haryana, India.
Published Online: May-June 2022
Pages: 167-171
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Abstract
View PDFAbstract: The development of latest technologies like data science, machine learning, and artificial intelligence has created new path for medical organization and healthcare institutions, to forecast the illness early as possible and it provide better treatment with advanced facilities. Quality of Forecast of illness goes down with less data. But with data everything is possible. Even detecting any illness with help of just symptoms is now possible. Many illness are area based, so accuracy goes down there also. Human body always give some kind of signal when there is something wrong in our body, when we have those signal like small fever or headache we need to take care of those signs else it can be too late to treat them. Many times patients treat symptoms as a minor issue and later on, he/she gets to know it was a major disease that led her to something very serious. So we are offering an illness forecast method that can forecast the illness depending on symptoms so they can be treated at the early stage, which is necessary as with help of this we can help someone to save his/her life or his/her friend life too. In this way with this forecast we can get treated only for those signs and illness which patient does have. This forecast method is not a replacement for real diagnosis but this method in place help patient to get treated for what is necessary and avoid complication on time rather than getting the cure for some other disease with help of symptoms he/she can get treatment for the exact disease which he/she likely to have. The accuracy of offered method is 95.12%. The method has huge possibility in detecting possible illness quite accurately. The objective of this offered method is to give a tool to people with less technical knowledge and a new doctor starting in his/her career can treat patient effectively.
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