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Random Forest is a supervised machine learning algorithm. From the name itself we are able to know that, the Random Fores! technique produces multiple decision trees before giving an output. This technique is based on the thing that more number of tree: would tend to the right decision. For classification, it uses a voting system and then decides the class whereas in regression it take: the mean of all the outputs of the decision trees. It works well with large datasets with high dimensionality [15]. Random forest algorithm steps

Figure 4 Random Forest is a supervised machine learning algorithm. From the name itself we are able to know that, the Random Fores! technique produces multiple decision trees before giving an output. This technique is based on the thing that more number of tree: would tend to the right decision. For classification, it uses a voting system and then decides the class whereas in regression it take: the mean of all the outputs of the decision trees. It works well with large datasets with high dimensionality [15]. Random forest algorithm steps

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The general flow of model includes home screen which contains the user login functionality and primary information of the diseases with the tabs for each and every disease. Every tab is linked with a separate page for the user to put all the symptoms and necessary info in the form structure. The tab contains About page, Diabetes, Cancer and Kidney named components which are further linked with separate HTML page. In the backend model will work with all the pre-processing, splitting data, classification and regression steps and predict if the person is affected or not accurately. If the person wants any help related to doctors, there is functionality of doctor’s suggestions with chatbot. Here, for the predictions, we have chosen the best fit algorithm for each disease. We have worked with algorithms like Logistic As the model is of multiple disease prediction, it is possible to predict more than one disease at a time. Users do not need to search for more than one website or application for diagnosing multiple diseases. With the model, the user is also getting the facility of chatbot and doctor recommendations.
The general flow of model includes home screen which contains the user login functionality and primary information of the diseases with the tabs for each and every disease. Every tab is linked with a separate page for the user to put all the symptoms and necessary info in the form structure. The tab contains About page, Diabetes, Cancer and Kidney named components which are further linked with separate HTML page. In the backend model will work with all the pre-processing, splitting data, classification and regression steps and predict if the person is affected or not accurately. If the person wants any help related to doctors, there is functionality of doctor’s suggestions with chatbot. Here, for the predictions, we have chosen the best fit algorithm for each disease. We have worked with algorithms like Logistic As the model is of multiple disease prediction, it is possible to predict more than one disease at a time. Users do not need to search for more than one website or application for diagnosing multiple diseases. With the model, the user is also getting the facility of chatbot and doctor recommendations.
Accuracy of each disease with each algorithm is given by:
Accuracy of each disease with each algorithm is given by:
Fig 2: Density graph of all variables for Diabetes
Fig 2: Density graph of all variables for Diabetes
Fig 3 : Density graph of all variables for Cancer
Fig 3 : Density graph of all variables for Cancer
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Multiple Classifiers
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