Rakesh Seminar.pptx
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This document describes a diabetes prediction system using machine learning. The system allows users to input their medical values and predicts whether they have diabetes or not. It uses an SVM classifier trained on a dataset to process the input values and make the prediction. The system aims to help people determine if their symptoms indicate diabetes or are normal body symptoms. It was created by a team of 3 students for their final year project, guided by Dr. Latha Maheswari.
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In developing countries like India, the plan of subsidising basic domestic commodities for poor families is an important part of meeting people’s basic needs. When a self-contained system for ration distribution is available, it benefits cardholders in a variety of ways. The E-Ration is the most convenient way to purchase ration items. Its goal is to offer ration products on the internet. E-Ration Shop is an automated system that distributes the exact amount of ration to cardholders based on the type of ration card and the number of family members, as well as keeping track of transactions in a database. As a result, this approach will reduce dealer communication, manual calculations, and time spent in stores. The cardholder can access the rationing system at any time during the month, with no need for human participation in the ration shop. Each transaction is automatically logged into the database by this system. Customers who purchase ration items online will have their information saved in an internet database, which will be viewable to higher-ranking officers as well as the shop owner. A fair price store (FPS) or ration shop is another name for a public distribution shop. It is a part of the Government of India's public distribution system, which provides subsidised rations to the needy in India. The Civil Supplies Corporation is a key government agency that oversees and distributes basic supplies to all inhabitants. Various products like as rice, sugar, and kerosene are supplied utilising a traditional ration store method in that system. Some of the drawbacks of the traditional ration shop system include: The user is unable to obtain an accurate quantity of material due to laborious measurements in the traditional technique. Because it is a direct process done by online ration shop keeper, this application uploads the data immediately to the server, confirming the data. Because it is a direct process done by online ration shop keeper, they cannot do anything in these transactions as they do in paper work.
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This document describes a diabetes prediction system using machine learning. The system allows users to input their medical values and predicts whether they have diabetes or not. It uses an SVM classifier trained on a dataset to process the input values and make the prediction. The system aims to help people determine if their symptoms indicate diabetes or are normal body symptoms. It was created by a team of 3 students for their final year project, guided by Dr. Latha Maheswari.
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In developing countries like India, the plan of subsidising basic domestic commodities for poor families is an important part of meeting people’s basic needs. When a self-contained system for ration distribution is available, it benefits cardholders in a variety of ways. The E-Ration is the most convenient way to purchase ration items. Its goal is to offer ration products on the internet. E-Ration Shop is an automated system that distributes the exact amount of ration to cardholders based on the type of ration card and the number of family members, as well as keeping track of transactions in a database. As a result, this approach will reduce dealer communication, manual calculations, and time spent in stores. The cardholder can access the rationing system at any time during the month, with no need for human participation in the ration shop. Each transaction is automatically logged into the database by this system. Customers who purchase ration items online will have their information saved in an internet database, which will be viewable to higher-ranking officers as well as the shop owner. A fair price store (FPS) or ration shop is another name for a public distribution shop. It is a part of the Government of India's public distribution system, which provides subsidised rations to the needy in India. The Civil Supplies Corporation is a key government agency that oversees and distributes basic supplies to all inhabitants. Various products like as rice, sugar, and kerosene are supplied utilising a traditional ration store method in that system. Some of the drawbacks of the traditional ration shop system include: The user is unable to obtain an accurate quantity of material due to laborious measurements in the traditional technique. Because it is a direct process done by online ration shop keeper, this application uploads the data immediately to the server, confirming the data. Because it is a direct process done by online ration shop keeper, they cannot do anything in these transactions as they do in paper work.
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This document describes a smart health disease prediction system that allows users to get instant guidance on potential health issues based on entered symptoms. The system uses data mining techniques to analyze symptoms and predict the most likely associated illness. The objectives are to provide a simple user interface for obtaining guidance and predicting diseases and recommending medicines. It aims to give users access to doctor assistance anytime and provide self-diagnosis through discussion of symptoms.
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Diabetes mellitus is a common disease caused by a set of metabolic ailments
where the sugar stages over drawn-out period is very high. It touches diverse organs
of the human body which therefore harm a huge number of the body's system, in
precise the blood strains and nerves. Early prediction in such disease can be exact
and save human life. To achieve the goal, this research work mainly discovers
numerous factors associated to this disease using machine learning techniques.
Machine learning methods provide effectual outcome to extract knowledge by building
predicting models from diagnostic medical datasets together from the diabetic
patients. Quarrying knowledge from such data can be valuable to predict diabetic
patients. In this research, six popular used machine learning techniques, namely
Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), C4.5 Decision
Tree (DT), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) are
compared in order to get outstanding machine learning techniques to forecast diabetic
mellitus. Our new outcome shows that Support Vector Machine (SVM) achieved
higher accuracy compared to other machine learning techniques.
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ppt presentation for diabetes.pptx
ppt presentation for diabetes prediction using machine learning,
This is a classification problem of supervised machine learning. The objective is to predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.
Machine learning can help people make a preliminary judgment about diabetes according to their daily physical examination data, and it can serve as a reference for doctors .
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This document provides background information on developing a preliminary diagnostic system for endocrine diseases. It discusses diabetes and thyroid disorders, which can cause nail discoloration, brittleness, and other changes. Previous research has found glycated keratin in fingernails can indicate tissue damage in diabetic patients. The objectives of this study are to design a prototype diagnostic system that captures fingernail images and temperature, analyzes for possible diabetes or thyroidism, and prints/emails results. The scope is limited to these two diseases and depends on severity levels for accuracy.
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where the sugar stages over drawn-out period is very high. It touches diverse organs
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This is a classification problem of supervised machine learning. The objective is to predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.
Machine learning can help people make a preliminary judgment about diabetes according to their daily physical examination data, and it can serve as a reference for doctors .
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1) The document proposes a system for multi-disease detection using deep learning that could provide early detection of chronic diseases like heart disease, cancer, and diabetes from medical data and save lives.
2) It reviews literature on disease prediction using machine learning algorithms like CNN, KNN, decision trees, and support vector machines. CNN showed slightly better accuracy than KNN for general disease detection.
3) The proposed system would use deep learning models to detect and classify diseases from medical images and data with high accuracy, helping doctors verify test results and enhancing their experience with diseases. It aims to reduce the costs of diagnostic testing for chronic conditions.
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- It analyzes reasons why people do not frequently visit doctors like lack of time and proposes this mobile system could help people check health anytime without visiting clinics.
- The system works by connecting health tools to smart devices, using apps to monitor vitals, perform basic checks, and connect to doctors for advice or video consultations depending on membership level.
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- The authors build several prediction models using machine learning algorithms like logistic regression, KNN, decision trees on a diabetes dataset to classify patients as having diabetes or not.
- They evaluate the performance of the different models using metrics like accuracy, and find that KNN achieved the highest accuracy of 78% on the test data.
- The document also reviews several other studies applying techniques like random forests, support vector machines, convolutional neural networks to the same diabetes prediction task and Pima Indian diabetes dataset.
- The authors conduct their own experiments applying algorithms like logistic regression, KNN, decision trees, random forest, XGBoost to the
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Machine learning algorithms play a vital role in prediction of many diseases such as heart disease, diabetes, cancer, lung disease etc. The applicability of machine learning algorithms to healthcare domain relieves the burden of physicians as it is impractical to scan manually all the data collected over a period of time in order to arrive at some valuable information. Machine learning algorithms learn from the training dataset and they become capable of thinking like a human. Once the algorithm completes it learning with training dataset, it can automatically predict the target output label of any unseen data. In this work, predicting diabetes using machine learning algorithms has been taken up. A conceptual architecture has been proposed based on big data architecture.
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DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
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With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
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How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
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- 1. DISEASE PREDICTION USING ML Rakesh Kumar Lenka 1901206101 CSE
- 2. Disease Prediction using Machine Learning is the system that is used to predict the diseases from the data which are given by the patients or any user. INTRODUCTION 2
- 3. CONTENT 3 Introduction Data Flow Diagram Why Diabetes Live Explanation Future Scope Advantages Disadvantages Conclusion References
- 4. DATAFLOW DIAGRAM 4 DATA FLOW DIAGRAM
- 5. WHY DIABETES *Source Internet 5 Prediction Using ML • Heart Disease • Lungs Cancer • Liver Disease • Stroke • Breast Cancer • Kidney Disease
- 6. FUTURE SCOPE 6 When ever a person do his/her health checkup the machine can predict the disease in which he/she can be affected in the future. Early Prediction The accuracy of the ML Model can be increased and it can become very efficient that we can trust the result. High Accuracy Rate Electronics health records consist of entire medical and health data in a single system to ensure data availability and accessibility. Electronic Health Record
- 7. ADVANTAGES OF DISEASE PREDICTION 7 • If we can predict a disease before hand then we can take necessary steps to avoid the disease and hence can save the lives of our dear one. Can Save Thousands Of Lives • Government can take mass measures to fight against the diseases that are going to prevail Govt. can get benefit • By predicting diseases we can actually improve people’s lifestyle up to a great extend. Can improve life style
- 8. DISADVANTAGES OF DISEASE PREDICTION 8 • As no model is 100% accurate so we can’t be very clear about the results. Accuracy Issues • Without huge data set we can’t get accurate results sometimes the model can predict wrong disease as well. Requires Big Amount Of Data • If the input dataset is wrong then the model will predict wrong disease accordingly. Wrong DataSet Issue
- 9. CONCLUSION 9 Disease prediction is a growing technology It can predict deadly disease and save lives But still a lot of improvements can be done in this filed Like diabetes we can also predict other diseases using this technique
- 10. 10 REFERENCES https://ieeexplore.ieee.org/ https://www.researchgate.net/ https://www.sciencedirect.com/ https://www.python.org/ https://jupyter.org/ https://www.pythonsoft.org/ https://www.slideshare.net/
- 11. THANK YOU Rakesh Kumar Lenka 6th Sem,CSE