The medical industry has seen considerable growth over the years, due to technological advancements and an increase in health problems. Due to people's busy and busy schedules, health problems have increased. But since almost everyone is carrying their smartphone these days, an Android health app can prove to be very beneficial. The intention behind this project is to create an Android application that can be used by people to manage their health. This system will use decision tree algorithm to predict diabetes. The user will have to answer a questionnaire which will consist of various parameters concerning the user's health. This app will include features such as ordering medications, making doctor appointments, medication and diet reminders. Decision trees use a tree structure to build classification models. It divides a data set into smaller subsets. The leaf node represents a decision. Based on the characteristic values ​​of the instances, decision trees classify the instances. Each node represents a feature in an instance of a decision tree that needs to be classified, and each branch represents a value. The classification of instances starts from the root node and is sorted based on their feature values. Categorical and numerical data can be processed by decision trees. Say no to plagiarism. Get a tailor-made essay on “Why violent video games should not be banned”?Get the original essayData mining is the method of examining huge pre-existing databases to generate new information. Diagnosing and predicting the health of an individual is an important objective of this project. This can be achieved using advanced decision tree machine learning algorithms. An Android app is one of the easiest methods to manage a person's health due to the increasing use of smartphones. The application will consist of a set of questions that the user must answer. This will then use decision trees which are a data mining algorithm. This will then immediately predict the possibility of diabetes. In addition to this, the app will also include features like diet monitoring, where a user can monitor their diet and the app will send notifications or reminders about it. Other features will be the provision of the doctor's appointment book. The app will also allow the user to order medications. Different factors like gender, age, blood sugar level, cholesterol, hereditary diseases and many more factors are taken into account in this proposed system. Thus to know if the user is prone to diabetes or not. The main objective is to predict the onset of diabetes and monitor the health status of a person based on the answers provided by the questionnaire user. Other features also include diet monitoring, doctor appointment scheduling, and medication ordering. Many prediction algorithms are used, but due to parameter consideration or inefficiency of the algorithm, the accuracy is not so high. Therefore, we consider many parameters and also use the C5 Classifier algorithm which gives high accuracy. The rest is divided into 6 sections. In Sections 2 and 3, related works and proposed works are presented and structured. In section 4, theproposed methodology is presented and details regarding the algorithm to be used are explained. In section 5, the conclusion and finally in section 6 the future work has been included. Android Application for Health Monitoring and Diabetes Prediction Using Data Mining Techniques [1], In this paper, the authors proposed a project that seeks to apply information and create an Android application that could be used by patients to manage their health problems and thus allow them to have a good life. The apps also create a system to predict whether a person is at risk of developing the disease diabetes over the next 10 to 15 years. The system uses the questionnaire method using Naive Bayes algorithm A data mining approach for heart disease prediction using neural networks [2], the authors proposed that a heart disease prediction system (HDPS ) is developed using the neural network. The HDPS system predicts the likelihood that a patient will have heart disease. For prediction, the system uses blood pressure, gender and 14 medical parameters such as cholesterol. Here, two additional parameters are added, namely smoking and obesity, for greater accuracy. From the results, it was found that the neural network predicted heart diseases with almost 100% accuracy. Disease prediction system using data mining techniques [3] proposed a research paper that uses data mining for better disease prediction. It uses medical data mining techniques such as classification, association rule mining and clustering is implemented to evaluate different types of heart-related problems. The survey of machine learning algorithms for disease diagnosis [4] provides us with a comparative evaluation of different machine learning algorithms for the diagnosis of different diseases such as diabetes, heart disease, liver disease, dengue and hepatitis using medical imaging. It draws attention to the suite of algorithms and machine learning tools used for disease analysis and the decision-making process accordingly. The C5.0 algorithm for an improved decision tree with feature selection and error reduction [5] compared ID3, C4.5 and C5.0 with each other. Among all these classifiers, C5.0 gives more accurate and efficient results. This research paper used C5.0 as the base classifier, so the proposed system will classify the result set with high accuracy and low memory usage. The feature selection technique assumes that the data contains many redundant features, so it removes features that do not provide any useful information in any context. This paper also uses the reduced error pruning technique which is used to solve the decision tree overfitting problem. Decision trees can handle both classification and regression problems. Decision trees only require a data table with which they will directly build a classifier. On the other hand, Naive Bayes requires you to construct a classification by hand. If given a bunch of tabular data, it will fail to select the best features that can be used for classification. So, for this application, diabetes prediction will be done using the decision tree of the C5 classifier. This application requires the user to respond to aquestionnaire which will be linked to the patient's symptoms and habits. This will be taken as input and using decision trees algorithm the possibility of diabetes can be predicted. The app has a feature that allows the user to enter their diet. This app will monitor it and give the required analysis. Medications consumed by the user will also be monitored. User can also book doctor appointment using this app. Appointment details will be sent to the doctor. Here are the system modules:1. Prediction2. Diagnosis3. Diet plan4. Reminders and alarm and5. Ordering medications. Whether the user is prone to diabetes or not will be accurately predicted by this system using prediction algorithm calculations. New users must register themselves in order to create their respective accounts in the system. Access to the system is provided to the user through a login interface. The user can log in using a username and password on which he will be directed to the system home page. The user can then enter data to predict diabetes. We mainly focus on Diabetes Mellitus type. User can also use additional features like maintaining diet, reminding dosage, calling for doctor appointment etc. The prediction will be made using the decision tree of the C5 classifier. Here are the parameters that would be considered in the dataset for the prediction algorithm:1. Gender2. Age3. BMI4. Blood pressure5. Smoking6. Weeklyexercise7. Consumption of salty foods8. Alcohol consumption9. Stress at work10. Family history of blood pressure/diabetes11. Pregnant (if female)12. Blood fat level13. Health Food Consumption 14. Fatty Food Consumption Decision trees come under supervised machine learning (this means we have to explain the input and output in the training data) where continuous division of data takes place based on of a certain condition/parameter. The tree has two entities, the leaves and the decision nodes. The end result is in the leaves. And decision nodes split the data. Suppose we need to determine whether a person is healthy or not. We have parameters such as age, type of food consumption and duration of exercise. Here we have a yes or no decision, this can be called a binary classification problem. The final output at the leaf level would be healthy or not. There are two main types of decision trees. The example above is of the binary type of classification (Yes/No), where the output is either healthy or unhealthy. But here the decision is variable, i.e. categorical, i.e. the output is continuous. Regression trees (continuous data types), steps for decision tree: Algorithm: Generate a decision tree from the training tuples of the data partition, D. Input: Data partition, D, is a set of training tuples and their associated class labels; attribute list, a set of candidate attributes; The attribute selection method is a procedure for determining whether the splitting criterion best divides tuples into individual classes. This criterion consists of a splitting attribute and, optionally, either a splitting point or a splitting subset. Output: A decision tree. Algorithm: (1) create a node N; (2) if the tuple in D are all of the same class, C, then (3) returns N as a leaf node labeled with class C; (4) if the attribute list is empty, then (5) returns N in.