This document summarizes a student's MCA IV semester project on developing a diabetes prediction system using machine learning techniques. The student proposes building a system that uses support vector machines and neural networks to accurately predict diabetes using a PIMA Indian diabetes dataset. The system is designed to have several modules, including an intelligent meal recommender, educational module with food recognition, activity tracking, and medication reminders. It aims to help diabetic patients manage their condition through personalized recommendations, education, and reminders.
Disease prediction in big data healthcare using extended convolutional neural...IJAAS Team
Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
Disease prediction in big data healthcare using extended convolutional neural...IJAAS Team
Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio and Pypark software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.
Diabetes Prediction by Supervised and Unsupervised Approaches with Feature Se...IJARIIT
Two approaches to building models for prediction of the onset of Type diabetes mellitus in juvenile subjects were examined. A set of tests performed immediately before diagnosis was used to build classifiers to predict whether the subject would be diagnosed with juvenile diabetes. A modified training set consisting of differences between test results taken at different times was also used to build classifiers to predict whether a subject would be diagnosed with juvenile diabetes. Supervised were compared with decision trees and unsupervised of both types of classifiers. In this study, the system and the test most likely to confirm a diagnosis based on the pre-test probability computed from the patient's information including symptoms and the results of previous tests. If the patient's disease post-test probability is higher than the treatment threshold, a diagnostic decision will be made, and vice versa. Otherwise, the patient needs more tests to help make a decision. The system will then recommend the next optimal test and repeat the same process. In this thesis find out which approach is better on diabetes dataset in weka framework. Also use feature selection techniques which reduce the features and complexities of process
K-Nearest Neighbours based diagnosis of hyperglycemiaijtsrd
AI or artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction. As a result, Artificial Intelligence is gaining Importance in science and engineering fields. The use of Artificial Intelligence in medical diagnosis too is becoming increasingly common and has been used widely in the diagnosis of cancers, tumors, hepatitis, lung diseases, etc... The main aim of this paper is to build an Artificial Intelligent System that after analysis of certain parameters can predict that whether a person is diabetic or not. Diabetes is the name used to describe a metabolic condition of having higher than normal blood sugar levels. Diabetes is becoming increasingly more common throughout the world, due to increased obesity - which can lead to metabolic syndrome or pre-diabetes leading to higher incidences of type 2 diabetes. Authors have identified 10 parameters that play an important role in diabetes and prepared a rich database of training data which served as the backbone of the prediction algorithm. Keeping in view this training data authors developed a system that uses the artificial neural networks algorithm to serve the purpose. These are capable of predicting new observations (on specific variables) from previous observations (on the same or other variables) after executing a process of so-called learning from existing training data (Haykin 1998).The results indicate that the performance of KNN method when compared with the medical diagnosis system was found to be 91%. This system can be used to assist medical programs especially in geographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results. Abid Sarwar"K-Nearest Neighbours based diagnosis of hyperglycemia" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7046.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/7046/k-nearest-neighbours-based-diagnosis-of-hyperglycemia/abid-sarwar
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.
Development of a Hybrid Dynamic Expert System for the Diagnosis of Peripheral...ijtsrd
This paper presents the development of a hybrid dynamic expert system for the diagnosis of peripheral diabetes and remedies using a rule based machine learning technique. The aim was to develop a solution to the risk factors of peripheral diabetes. The methodology applied in this study is the experimental method, and the software design methodology used was the agile methodology. Data was collected from Nnamdi Azikiwe University Teaching Hospitals NAUTH and the Lagos State University Teaching Hospital LASUTH for patients between the ages of 28 87years suffering from peripheral neuropathy. Other methods used were data integration by applying uniform data access UDA technique, data processing using Infinite Impulse Response Filter IIRF , data extraction with a computerized approach, machine learning algorithm with Dynamic Feed Forward Neural Network DFNN , rule base algorithm. The modeling of the hybrid dynamic expert system and remedies was achieved using the DFNN for the detection of DPN and a rule based model for remedies and recommendations. The models were implemented with MATLAB and Java programming languages. The result when evaluated achieved a Mean Square Error MSE of 4.9392e 11 and Regression R of 0.99823. The implication of the result showed that the peripheral diabetes detection model correctly learns the peripheral diabetes attributes and was also able to correctly detect peripheral diabetes in patients. The model when compared with other sophisticated models also showed that it achieved a better regression score. The reason was due to the appropriate steps used in the data preparation such as integration and the use of IIFR filter, feature extraction, and the deep configuration of the regression model. Omeye Emmanuel C. | Ngene John N. | Dr. Anyaragbu Hope U. | Dr. Ozioko Ekene | Dr. Iloka Bethram C. | Prof. Inyiama Hycent C. "Development of a Hybrid Dynamic Expert System for the Diagnosis of Peripheral Diabetes and Remedies using a Rule-Based Machine Learning Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-7 , December 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52356.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/52356/development-of-a-hybrid-dynamic-expert-system-for-the-diagnosis-of-peripheral-diabetes-and-remedies-using-a-rulebased-machine-learning-technique/omeye-emmanuel-c
A Tentative analysis of Liver Disorder using Data Mining Algorithms J48, Deci...MangaiK4
Abstract — Nowadays healthcare field has additional data mining process became a crucial role to use for disease prediction. Data mining is that the process of investigate up info from the huge information sets. The medical information is extremely voluminous. Therefore the investigator is extremely difficult to predict the disease is challenging. To overcome this issue the researchers use data mining processing technique like classification, clustering, association rules so on. The most objective of this analysis work is to predict disease supported common attributes intake of alcohol, smoking, obesity, diabetes, consumption of contaminated food, case history of liver disease using classification algorithm. The algorithms employed in this analysis work are J48, Naive Bayes. These classification algorithms are compared base on the performance factors accuracy and execution time. The investigational results could be a improved classifier for predict the liver disease.
A Neural Network Based Diabetes Prediction on Imbalance Dataset.pptxshivani28yadav
Presented this research paper presentation in 10th International Conference on Communication Systems and Network Technologies(CSNT)-2021 .This research work was published by IEEE .The link mentioned below:
https://ieeexplore.ieee.org/document/9509732
As an extensively well-known chronic disease, diabetes is an illness that harms the body’s capability to process blood glucose. The proper treatment of diabetes could help a person live a long and normal life in general. It is necessary to detect the disease at an early stage. We focus our work on the performance of a machine-learning (ML) algorithm to identify the presence of diabetes on the PIMA Indian diabetes dataset (PIDD) which referenced from the University of California, Irvine (UCI) ML repository. Using ML, we know about the classification and prediction techniques. Further, diabetes became an attention seeker in the field of research due to the presence of imbalanced and missing data. Although many factors affect the performance of the algorithm, This research paper worked on the prediction technique for diabetes classification with outliers and missing values in data with class imbalance. Using an adaptive synthetic sampling method (ADASYN) and reduced the impact of class imbalance on the performance of the prediction model. Then, this algorithm improved the generalization using a feature selection technique and multilayer perceptron classifiers to make predictions and evaluations. Experimental results shows that this experiment obtained a better accuracy of 84% with a neural network model in comparison with the previous model.
Keywords are Diabetes prediction . Machine Learning. Outliers . Artificial Neural Network . Adaptive synthetic sampling . Multilayer Perceptron
Diabetes Prediction by Supervised and Unsupervised Approaches with Feature Se...IJARIIT
Two approaches to building models for prediction of the onset of Type diabetes mellitus in juvenile subjects were examined. A set of tests performed immediately before diagnosis was used to build classifiers to predict whether the subject would be diagnosed with juvenile diabetes. A modified training set consisting of differences between test results taken at different times was also used to build classifiers to predict whether a subject would be diagnosed with juvenile diabetes. Supervised were compared with decision trees and unsupervised of both types of classifiers. In this study, the system and the test most likely to confirm a diagnosis based on the pre-test probability computed from the patient's information including symptoms and the results of previous tests. If the patient's disease post-test probability is higher than the treatment threshold, a diagnostic decision will be made, and vice versa. Otherwise, the patient needs more tests to help make a decision. The system will then recommend the next optimal test and repeat the same process. In this thesis find out which approach is better on diabetes dataset in weka framework. Also use feature selection techniques which reduce the features and complexities of process
K-Nearest Neighbours based diagnosis of hyperglycemiaijtsrd
AI or artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction. As a result, Artificial Intelligence is gaining Importance in science and engineering fields. The use of Artificial Intelligence in medical diagnosis too is becoming increasingly common and has been used widely in the diagnosis of cancers, tumors, hepatitis, lung diseases, etc... The main aim of this paper is to build an Artificial Intelligent System that after analysis of certain parameters can predict that whether a person is diabetic or not. Diabetes is the name used to describe a metabolic condition of having higher than normal blood sugar levels. Diabetes is becoming increasingly more common throughout the world, due to increased obesity - which can lead to metabolic syndrome or pre-diabetes leading to higher incidences of type 2 diabetes. Authors have identified 10 parameters that play an important role in diabetes and prepared a rich database of training data which served as the backbone of the prediction algorithm. Keeping in view this training data authors developed a system that uses the artificial neural networks algorithm to serve the purpose. These are capable of predicting new observations (on specific variables) from previous observations (on the same or other variables) after executing a process of so-called learning from existing training data (Haykin 1998).The results indicate that the performance of KNN method when compared with the medical diagnosis system was found to be 91%. This system can be used to assist medical programs especially in geographically remote areas where expert human diagnosis not possible with an advantage of minimal expenses and faster results. Abid Sarwar"K-Nearest Neighbours based diagnosis of hyperglycemia" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7046.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/7046/k-nearest-neighbours-based-diagnosis-of-hyperglycemia/abid-sarwar
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.
Development of a Hybrid Dynamic Expert System for the Diagnosis of Peripheral...ijtsrd
This paper presents the development of a hybrid dynamic expert system for the diagnosis of peripheral diabetes and remedies using a rule based machine learning technique. The aim was to develop a solution to the risk factors of peripheral diabetes. The methodology applied in this study is the experimental method, and the software design methodology used was the agile methodology. Data was collected from Nnamdi Azikiwe University Teaching Hospitals NAUTH and the Lagos State University Teaching Hospital LASUTH for patients between the ages of 28 87years suffering from peripheral neuropathy. Other methods used were data integration by applying uniform data access UDA technique, data processing using Infinite Impulse Response Filter IIRF , data extraction with a computerized approach, machine learning algorithm with Dynamic Feed Forward Neural Network DFNN , rule base algorithm. The modeling of the hybrid dynamic expert system and remedies was achieved using the DFNN for the detection of DPN and a rule based model for remedies and recommendations. The models were implemented with MATLAB and Java programming languages. The result when evaluated achieved a Mean Square Error MSE of 4.9392e 11 and Regression R of 0.99823. The implication of the result showed that the peripheral diabetes detection model correctly learns the peripheral diabetes attributes and was also able to correctly detect peripheral diabetes in patients. The model when compared with other sophisticated models also showed that it achieved a better regression score. The reason was due to the appropriate steps used in the data preparation such as integration and the use of IIFR filter, feature extraction, and the deep configuration of the regression model. Omeye Emmanuel C. | Ngene John N. | Dr. Anyaragbu Hope U. | Dr. Ozioko Ekene | Dr. Iloka Bethram C. | Prof. Inyiama Hycent C. "Development of a Hybrid Dynamic Expert System for the Diagnosis of Peripheral Diabetes and Remedies using a Rule-Based Machine Learning Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-7 , December 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52356.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/52356/development-of-a-hybrid-dynamic-expert-system-for-the-diagnosis-of-peripheral-diabetes-and-remedies-using-a-rulebased-machine-learning-technique/omeye-emmanuel-c
A Tentative analysis of Liver Disorder using Data Mining Algorithms J48, Deci...MangaiK4
Abstract — Nowadays healthcare field has additional data mining process became a crucial role to use for disease prediction. Data mining is that the process of investigate up info from the huge information sets. The medical information is extremely voluminous. Therefore the investigator is extremely difficult to predict the disease is challenging. To overcome this issue the researchers use data mining processing technique like classification, clustering, association rules so on. The most objective of this analysis work is to predict disease supported common attributes intake of alcohol, smoking, obesity, diabetes, consumption of contaminated food, case history of liver disease using classification algorithm. The algorithms employed in this analysis work are J48, Naive Bayes. These classification algorithms are compared base on the performance factors accuracy and execution time. The investigational results could be a improved classifier for predict the liver disease.
A Neural Network Based Diabetes Prediction on Imbalance Dataset.pptxshivani28yadav
Presented this research paper presentation in 10th International Conference on Communication Systems and Network Technologies(CSNT)-2021 .This research work was published by IEEE .The link mentioned below:
https://ieeexplore.ieee.org/document/9509732
As an extensively well-known chronic disease, diabetes is an illness that harms the body’s capability to process blood glucose. The proper treatment of diabetes could help a person live a long and normal life in general. It is necessary to detect the disease at an early stage. We focus our work on the performance of a machine-learning (ML) algorithm to identify the presence of diabetes on the PIMA Indian diabetes dataset (PIDD) which referenced from the University of California, Irvine (UCI) ML repository. Using ML, we know about the classification and prediction techniques. Further, diabetes became an attention seeker in the field of research due to the presence of imbalanced and missing data. Although many factors affect the performance of the algorithm, This research paper worked on the prediction technique for diabetes classification with outliers and missing values in data with class imbalance. Using an adaptive synthetic sampling method (ADASYN) and reduced the impact of class imbalance on the performance of the prediction model. Then, this algorithm improved the generalization using a feature selection technique and multilayer perceptron classifiers to make predictions and evaluations. Experimental results shows that this experiment obtained a better accuracy of 84% with a neural network model in comparison with the previous model.
Keywords are Diabetes prediction . Machine Learning. Outliers . Artificial Neural Network . Adaptive synthetic sampling . Multilayer Perceptron
Navigating Challenges: Mental Health, Legislation, and the Prison System in B...Guillermo Rivera
This conference will delve into the intricate intersections between mental health, legal frameworks, and the prison system in Bolivia. It aims to provide a comprehensive overview of the current challenges faced by mental health professionals working within the legislative and correctional landscapes. Topics of discussion will include the prevalence and impact of mental health issues among the incarcerated population, the effectiveness of existing mental health policies and legislation, and potential reforms to enhance the mental health support system within prisons.
Telehealth Psychology Building Trust with Clients.pptxThe Harvest Clinic
Telehealth psychology is a digital approach that offers psychological services and mental health care to clients remotely, using technologies like video conferencing, phone calls, text messaging, and mobile apps for communication.
CHAPTER 1 SEMESTER V - ROLE OF PEADIATRIC NURSE.pdfSachin Sharma
Pediatric nurses play a vital role in the health and well-being of children. Their responsibilities are wide-ranging, and their objectives can be categorized into several key areas:
1. Direct Patient Care:
Objective: Provide comprehensive and compassionate care to infants, children, and adolescents in various healthcare settings (hospitals, clinics, etc.).
This includes tasks like:
Monitoring vital signs and physical condition.
Administering medications and treatments.
Performing procedures as directed by doctors.
Assisting with daily living activities (bathing, feeding).
Providing emotional support and pain management.
2. Health Promotion and Education:
Objective: Promote healthy behaviors and educate children, families, and communities about preventive healthcare.
This includes tasks like:
Administering vaccinations.
Providing education on nutrition, hygiene, and development.
Offering breastfeeding and childbirth support.
Counseling families on safety and injury prevention.
3. Collaboration and Advocacy:
Objective: Collaborate effectively with doctors, social workers, therapists, and other healthcare professionals to ensure coordinated care for children.
Objective: Advocate for the rights and best interests of their patients, especially when children cannot speak for themselves.
This includes tasks like:
Communicating effectively with healthcare teams.
Identifying and addressing potential risks to child welfare.
Educating families about their child's condition and treatment options.
4. Professional Development and Research:
Objective: Stay up-to-date on the latest advancements in pediatric healthcare through continuing education and research.
Objective: Contribute to improving the quality of care for children by participating in research initiatives.
This includes tasks like:
Attending workshops and conferences on pediatric nursing.
Participating in clinical trials related to child health.
Implementing evidence-based practices into their daily routines.
By fulfilling these objectives, pediatric nurses play a crucial role in ensuring the optimal health and well-being of children throughout all stages of their development.
One of the most developed cities of India, the city of Chennai is the capital of Tamilnadu and many people from different parts of India come here to earn their bread and butter. Being a metropolitan, the city is filled with towering building and beaches but the sad part as with almost every Indian city
Struggling with intense fears that disrupt your life? At Renew Life Hypnosis, we offer specialized hypnosis to overcome fear. Phobias are exaggerated fears, often stemming from past traumas or learned behaviors. Hypnotherapy addresses these deep-seated fears by accessing the subconscious mind, helping you change your reactions to phobic triggers. Our expert therapists guide you into a state of deep relaxation, allowing you to transform your responses and reduce anxiety. Experience increased confidence and freedom from phobias with our personalized approach. Ready to live a fear-free life? Visit us at Renew Life Hypnosis..
QA Paediatric dentistry department, Hospital Melaka 2020Azreen Aj
QA study - To improve the 6th monthly recall rate post-comprehensive dental treatment under general anaesthesia in paediatric dentistry department, Hospital Melaka
The dimensions of healthcare quality refer to various attributes or aspects that define the standard of healthcare services. These dimensions are used to evaluate, measure, and improve the quality of care provided to patients. A comprehensive understanding of these dimensions ensures that healthcare systems can address various aspects of patient care effectively and holistically. Dimensions of Healthcare Quality and Performance of care include the following; Appropriateness, Availability, Competence, Continuity, Effectiveness, Efficiency, Efficacy, Prevention, Respect and Care, Safety as well as Timeliness.
Antibiotic Stewardship by Anushri Srivastava.pptxAnushriSrivastav
Stewardship is the act of taking good care of something.
Antimicrobial stewardship is a coordinated program that promotes the appropriate use of antimicrobials (including antibiotics), improves patient outcomes, reduces microbial resistance, and decreases the spread of infections caused by multidrug-resistant organisms.
WHO launched the Global Antimicrobial Resistance and Use Surveillance System (GLASS) in 2015 to fill knowledge gaps and inform strategies at all levels.
ACCORDING TO apic.org,
Antimicrobial stewardship is a coordinated program that promotes the appropriate use of antimicrobials (including antibiotics), improves patient outcomes, reduces microbial resistance, and decreases the spread of infections caused by multidrug-resistant organisms.
ACCORDING TO pewtrusts.org,
Antibiotic stewardship refers to efforts in doctors’ offices, hospitals, long term care facilities, and other health care settings to ensure that antibiotics are used only when necessary and appropriate
According to WHO,
Antimicrobial stewardship is a systematic approach to educate and support health care professionals to follow evidence-based guidelines for prescribing and administering antimicrobials
In 1996, John McGowan and Dale Gerding first applied the term antimicrobial stewardship, where they suggested a causal association between antimicrobial agent use and resistance. They also focused on the urgency of large-scale controlled trials of antimicrobial-use regulation employing sophisticated epidemiologic methods, molecular typing, and precise resistance mechanism analysis.
Antimicrobial Stewardship(AMS) refers to the optimal selection, dosing, and duration of antimicrobial treatment resulting in the best clinical outcome with minimal side effects to the patients and minimal impact on subsequent resistance.
According to the 2019 report, in the US, more than 2.8 million antibiotic-resistant infections occur each year, and more than 35000 people die. In addition to this, it also mentioned that 223,900 cases of Clostridoides difficile occurred in 2017, of which 12800 people died. The report did not include viruses or parasites
VISION
Being proactive
Supporting optimal animal and human health
Exploring ways to reduce overall use of antimicrobials
Using the drugs that prevent and treat disease by killing microscopic organisms in a responsible way
GOAL
to prevent the generation and spread of antimicrobial resistance (AMR). Doing so will preserve the effectiveness of these drugs in animals and humans for years to come.
being to preserve human and animal health and the effectiveness of antimicrobial medications.
to implement a multidisciplinary approach in assembling a stewardship team to include an infectious disease physician, a clinical pharmacist with infectious diseases training, infection preventionist, and a close collaboration with the staff in the clinical microbiology laboratory
to prevent antimicrobial overuse, misuse and abuse.
to minimize the developme
Medical Technology Tackles New Health Care Demand - Research Report - March 2...pchutichetpong
M Capital Group (“MCG”) predicts that with, against, despite, and even without the global pandemic, the medical technology (MedTech) industry shows signs of continuous healthy growth, driven by smaller, faster, and cheaper devices, growing demand for home-based applications, technological innovation, strategic acquisitions, investments, and SPAC listings. MCG predicts that this should reflects itself in annual growth of over 6%, well beyond 2028.
According to Chris Mouchabhani, Managing Partner at M Capital Group, “Despite all economic scenarios that one may consider, beyond overall economic shocks, medical technology should remain one of the most promising and robust sectors over the short to medium term and well beyond 2028.”
There is a movement towards home-based care for the elderly, next generation scanning and MRI devices, wearable technology, artificial intelligence incorporation, and online connectivity. Experts also see a focus on predictive, preventive, personalized, participatory, and precision medicine, with rising levels of integration of home care and technological innovation.
The average cost of treatment has been rising across the board, creating additional financial burdens to governments, healthcare providers and insurance companies. According to MCG, cost-per-inpatient-stay in the United States alone rose on average annually by over 13% between 2014 to 2021, leading MedTech to focus research efforts on optimized medical equipment at lower price points, whilst emphasizing portability and ease of use. Namely, 46% of the 1,008 medical technology companies in the 2021 MedTech Innovator (“MTI”) database are focusing on prevention, wellness, detection, or diagnosis, signaling a clear push for preventive care to also tackle costs.
In addition, there has also been a lasting impact on consumer and medical demand for home care, supported by the pandemic. Lockdowns, closure of care facilities, and healthcare systems subjected to capacity pressure, accelerated demand away from traditional inpatient care. Now, outpatient care solutions are driving industry production, with nearly 70% of recent diagnostics start-up companies producing products in areas such as ambulatory clinics, at-home care, and self-administered diagnostics.
R3 Stem Cells and Kidney Repair A New Horizon in Nephrology.pptxR3 Stem Cell
R3 Stem Cells and Kidney Repair: A New Horizon in Nephrology" explores groundbreaking advancements in the use of R3 stem cells for kidney disease treatment. This insightful piece delves into the potential of these cells to regenerate damaged kidney tissue, offering new hope for patients and reshaping the future of nephrology.
Artificial Intelligence to Optimize Cardiovascular Therapy
DiabetesPPT.pptx
1. Student Name : Ganesh Guturu
Roll No : 20BF1F0037
Seminar Title : Diabetes prediction system
( Python, Django and Machine Learning )
Guide Name : P. Ravindra
MCA IV Semester Project I Review Presentation
2. ABSTRACT
-> Diabetes mellitus is the most common disease worldwide and keeps increasing
everyday due to changing lifestyles, unhealthy food habits and over weight
problems.
-> In the proposed system, an efficient way of detecting diabetes is proposed
through machine learning and deep leaning. Under machine learning, we used the
classification algorithm Support Vector machine (SVM) and neural network (NN) for
deep learning algorithm.
-> The experiment results shows that the prediction of diabetes done at high
accuracy.
3. Diabetes mellitus is a chronic, lifelong disease caused by excessively high blood sugar
levels. Diabetes is a disease that affects the body's ability to produce the hormone
insulin, thereby making carbohydrate metabolism abnormal and raising blood sugar
levels. As reported by the World Health Organization in 2020 were 463 millions are
with diabetes, 1.5 million deaths, the report indicates that is not difficult to guess how
much diabetes is very serious and chronic.
Many researchers conduct experiments to diagnose diseases using different machine
learning approach classification algorithms such as K-Nearest Neighbor (KNN), Logistic
Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Gradient Boosting
(GB) and Random Forest (RF) because researchers have proven demonstrated that
machine learning algorithms are more effective.
Introduction
4. Doctors rely on common knowledge for treatment. When common knowledge is
lacking, studies are summarized after some number of cases have been studied. But
this process takes time,
whereas if machine learning is used
-> the patterns can be identified earlier.
-> For using machine learning, a huge amount of data is required. There is very
limited amount of data available depending on the disease. Also,
-> The number of samples having no diseases is very high compared to number of
samples actually having the disease.
Problem Statement
5. -> Data mining approach like clustering, classification were studied in existing system.
-> Diabetes prediction using algorithms such as k- Nearest Neighbour (k-NN), k-
means, branch and bound algorithm was proposed.
-> A basic diabetic dataset is chosen for carrying out the comparative analysis.
-> The importance of feature analysis for predicting diabetes by employing machine
learning technique is discussed.
Existing system
6. -> The proposed system study is classification of Indian PIMA dataset for diabetes as
binary classification problem.
-> This is proposed to achieve through machine learning and deep learning
classification algorithm.
-> For machine learning, SVM algorithm is proposed
-> For deep learning, Neural network is used.
-> The proposed system improves accuracy of prediction through deep learning
techniques.
Proposed system
7. 1. Windows 9 or higher
2. Python 3.9 and related libraries
3. Anaconda
4. IDE ( PyCharm )
5. Web Browser
Software Requirements
8. DELL Inspiron 15 3000 laptop (Intel)
Processor : 11th Gen Intel® Core™ i3-1115G4
Display : 15.6", FHD 1920x1080
Memory : 8 GB, 1 x 8 GB, DDR4, 2666 MHz
Hard Drive : 512 GB, M.2, PCIe NVMe, SSD
Input device : Standard Keyboard and Mouse
Hardware Requirements
9. System Design and Development :
1. The System’s Requirements and Design Analysis
-> Scheduling and reminding diabetic patients to take their medication and blood
glucose readings,
-> recommending healthy meals for diabetics to keep their blood glucose levels in
check, and
-> encouraging and tracking the activity of diabetic patients,
-> providing a visual interface to help them make meaning of their readings and
establishing a sufficient connection between the doctor and the diabetic patient using
e-mail.
Modules
10. 2. System Architecture
Sub Modules
-> Diabetes Intelligent Meal
Recommender Module,
-> Educational Module for
Diabetics with Food Recognition
Engine,
-> Activity Tracking Module, and
-> Medication Reminder Module.
11. 3. The Diabetes Intelligent Meal Recommender Module
Sub Modules
3.1. Data Collection
3.2. Calorie Requirements
Determination
3.3. K-Nearest Neighbour
Algorithm
12. 4. The Educational Module with Food Recognition Capabilities
UML Diagrams
13. 5. The Activity and Reminder Module
-> Activity Tracker -> Medication Reminder
14. The testing results are categorized into
(1) Diabetes Intelligent Meal Recommender Module results,
(2) Educational Module for Diabetics with Food Recognition Engine results,
(3) Activity Tracking Module results, and
(4) Medication Reminder Module results.
Testing Strategies