Diabetes Mellitus (TY2) has secured the status of a global pandemic. Thus, the diagnosis of the disease at an early stage plays a very significant role as the early prediction of the disease will minimize the health risks associated with the disease. This projects aims to predict whether a person is diabetic or not via six different classification algorithms namely Support Vector Machine(SVC), K- Nearest Neighbour(Knn), Decision Tree, Naive Bayes, Logistic Regression and Random Forest. Moreover, early prediction of the diabetes disease with a higher accuracy can be found using various machine learning techniques.

Heart disease is one of the world's and our country's leading causes of death. Heart disease is caused by diabetes, genetics, high blood pressure and high cholesterol. The majority of the time, heart diseases occur without causing any symptoms. These circumstances can result in major health issues and even death. I will provide a valuable application in the field of preserving public health with this estimating program by enabling early identification of heart disease, which is the most common today and whose symptoms are quite variable. I aimed to prevent possible bad results with early diagnosis. The users are doctors and potential patients with the necessary health data for prediction. The dataset I use in the project is the Heart Disease dataset I got from the UCI Machine Learning Repository. Although there are 76 attributes in this dataset, all published studies only use a subset of 14 of them and dataset consist of 303 rows. The input of the project is the health data we receive from the user in line with the features in dataset. The output of the project is the result of either there is a risk of heart disease or there is no risk of heart disease, which will be calculated and returned according to the data entered by the user as a result of all operations. The machine learning model I chose to use is Linear SVM. While choosing the most suitable algorithm for the project, I considered the training period, the number of features, the output of the project, the number of columns and parameters in the dataset, and the inputs and output of the project. In this way, I tried many suitable algorithms and decided to use the Linear SVM (Support Vector Machine) algorithm with the highest performance and accuracy percentage. Using this algorithm, I achieved almost 86% accuracy. I found this algorithm suitable for the project, because SVM is a classification algorithm. It tries to find the best line called hyperplane separating the two classes. The algorithm ensures that the line to be drawn is set to pass from the farthest place to its elements in two classes. In the project there are 2 classes, those at risk of heart disease and those without. Linear SVM is used for linearly separable data like in the Project. Lastly, I learned the tkinter library and coded the GUI to bring these codes to the user and to create my application.

Machinery learning is a fast-expanding area that will change the method for the diagnosis and management of this chronic condition by applying itself to diabetes as a global pandemic. Machine learning principles have been used to build algorithms to help predictive models of the likelihood of diabetes development or related complications. Digital therapy has shown to be a well-established lifestyle care intervention for diabetes control. Patients are becoming more self-managed, and the assistance of therapeutic decision-making is available to both them and health care practitioners. Machine learning helps patient signs and bio-markers to persist, unburdened, remotely controlled. Social networking and online forums also increase patient commitment to the treatment of diabetes. Development in technologies helped to optimize the use of diabetes tools. These smart technological reforms together have led to an improved glycemic regulation, a decrease in fast glucose and glycosylated hemoglobin levels. Machine learning introduces a change in diabetes treatment model from traditional management techniques to data-driven care growth The trouble with medicines is that various drug formulations can cure the condition in several ways. As the diabetic population grows, new medications are increasingly emerging. In order to treat common diseases such as elevated cholesterol and high blood pressure, diabetics also continue to take other drugs. With the patient's age and other physical conditions, the potency of these medicines varies In this method, the effectiveness, risks of side effects and costs are measured side by side, and are readily grasped by doctors and patients. The most prevalent form of Type 2 diabetes effects more people as people grow up. This disease has also escalated dramatically due to the spread of western diets and lifestyles to developing countries. Diabetes is an incurable metabolic illness that happens when high blood sugar is present, and may have deadly effects. Today, medicine, nutritious diets and exercise will regulate diabetes. It is also unpredictable to choose and administer the most appropriate mixture of prescription, which is stable, cheap and well tolerated by patients as well By applying an adequate methodology for the design and development of systems experts can achieve objectives satisfactorily, as in the case of the Weiss and Kuligowski methodology. On the other hand, machine learning has several knowledge machine algorithms, which can be useful to be applied on various data sets through the different interfaces that offers, as the option of Explorer and Datasets, which were worked in this case of study, or to be included in other applications. Furthermore, both tools, contain what is necessary to conduct data transformations, grouping, regression, clustering, correlation and visualization tasks. Because they are designed as extensibility-oriented tools which allows to add new functionalities to a project, because it can be combined with other programming languages such as Prolog, for generation more robust expert systems Readmitted diabetes patients Machine learning techniques allow to automatically identify patterns and even make predictions based on a large amount of data that could be extracted from the computer systems used to ascertain information on readmission of diabetes patients. The analysis Clustering or grouping is a technique that allows exploring a setoff objects to determine if there are groups that can be significantly represented by certain characteristics, in this way, objects of the same group are very similar to each other and different from objects in other groups. The results obtained by comparing the relevance of different attributes as well as the use of two of the most popular algorithms in the world of machine learning are presented: neural networks and decision trees. Automatic classification of blood glucose measurements will allow specialists to prescribe a more accurate treatment based on the information obtained directly from the patients' glucometer (Hosseini et al, 2020). Thus, it contributes to the development of automatic decision support systems for gestational diabetes. This high level of glucose in the blood is transferred to the fetus causing various disorders: excessive growth of adipose tissues, which increases the need for caesarean section, neonatal hypoglycemia and increased risk of intrauterine fetal death (Dagliati et al, 2018). It also increases the risk of type 2 diabetes once the gestation period is over for both the mother and the fetus. The project proposes the development of intelligent and educational tools for the survey based on neurodiffuse techniques integrated into a telemedicine system. Telemedicine systems have been used with success on numerous occasions in diabetes and the integration of decision support tools in this type of system helps a better interpretation of the data (Abhari et al, 2019).

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Built a Multiple Disease Prediction system webapp using streamlit which predicts test results based on models built for multiple diseases like heart disease, diabetes, and Parkinson's disease using Support Vector Classification and Logistic Regression machine learning algorithms

Machine learning has been one of the standard and improving techniques with strong methods for classification and reorganization based on recursive learning. Machine learning allows to train and test classification system, with Artificial Intelligence. Machine learning has provided greatest support for predicting disease with correct case of training and testing. Diabetes needs greatest support of machine learning to detect diabetes disease in early stage, since it cannot be cured and also brings great complication to our health system. One of the promising techniques in machine learning is Random, it is a classification and regression algorithm consisting of many decisions trees. It uses bagging and feature randomness when building each individual tree to try to create an uncorrelated forest of trees whose prediction by committee is more accurate than that of any individual tree.

Multiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms. Topics machine-learning deep-learning cnn neural-networks breast-cancer-prediction classification-model diabetes-prediction .

A machine learning model that is predicting diabetes disease, whether one has diabetes or not. It is a classification model that uses several algorithms for prediction.

Developed a web-based Multiple Disease Prediction system using Streamlit, employing Support Vector Classification and Logistic Regression algorithms to forecast test outcomes for conditions such as heart disease, diabetes, and Parkinson’s disease.

Classification and Prediction of a person have a particular disease or not based on input by user. I have used Supervised machine learning algorithms, and evaluating performances of those algorithms. Diseases this model can predict: Diabetes, Heart Disease, Parkinsons. Software used: Python, Streamlit.

This project is a Machine Learning based Multiple Disease Prediction System that predicts diseases such as diabetes, heart disease, and Parkinson’s using medical input data. The model is trained using classification algorithms like Logistic Regression and SVM to assist in early disease detection and healthcare decision support.

A machine learning project that predicts the likelihood of diabetes using health parameters such as age, BMI, blood pressure, and glucose level. The model is trained on structured data to demonstrate early disease prediction using classification algorithms

For our PAI course project, we are building several disease prediction systems, including heart disease, diabetes, Parkinson's, and breast cancer classification. Using machine learning algorithms, we aim to analyze patient data and improve the accuracy of early diagnosis, providing valuable insights to healthcare professionals.

This project is a machine learning based system that predicts diseases based on symptoms or medical data. It uses classification algorithms trained on a dataset to provide accurate predictions for diseases such as diabetes, heart disease, and other common health conditions. The system includes a GUI for easy user interaction

Almost all systems that predict heart diseases using clinical dataset having parameters and inputs from complex tests conducted in labs. None of the systems predicts heart diseases supporting risk factors like age, case history, diabetes, hypertension, high cholesterol, tobacco smoking, alcohol intake, obesity or physical inactivity, etc. Heart disease patients have many of those visible risk factors in common which may be used very effectively for diagnosis. A system based on such risk factors would not only help medical professionals but it would give patients a warning about the probable presence of heart disease even before the patient visits a hospital or goes for costly medical check-ups. Hence this study presents a technique for prediction of heart disease using major risk factors with help of different Classifying Algorithms. This technique involves four major classification algorithms such as K Neighbors, Support Vector, Decision Tree, Random Forest algorithms.