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.
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
Diabetes Prediction Using Machine Learningjagan477830
Our proposed system aims at Predicting the number of Diabetes patients and eliminating the risk of False Negatives Drastically.
In proposed System, we use Random forest, Decision tree, Logistic Regression and Gradient Boosting Classifier to classify the Patients who are affected with Diabetes or not.
Random Forest and Decision Tree are the algorithms which can be used for both classification and regression.
The dataset is classified into trained and test dataset where the data can be trained individually, these algorithms are very easy to implement as well as very efficient in producing better results and can able to process large amount of data.
Even for large dataset these algorithms are extremely fast and can able to give accuracy of about over 90%.
Diabetes is a disease which is rapidly increasing all over the world. It occurs when pancreas does not produce sufficient insulin, or body can not sufficiently use insulin it produces. Diabetes person has increase blood glucose in the body. One of the major problem diabetic patients suffers from is the Diabetic Retinopathy (DR) and blindness. Since the number of diabetes patients is continuously increasing, it increases the data as well.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical
research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more
number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well
as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
Heart is the most important organ of a human body. It circulates oxygen and other vital nutrients through blood to different parts of the body and helps in the metabolic activities. Apart from this it also helps in removal of the metabolic wastes. Thus, even minor problems in heart can affect the whole organism. Researchers are diverting a lot of data analysis work for assisting the doctors to predict the heart problem. So, an analysis of the data related to different health problems and its functioning can help in predicting with a certain probability for the wellness of this organ. In this paper we have analysed the different prescribed data of 1094 patients from different parts of India. Using this data, we have built a model which gets trained using this data and tries to predict whether a new out-of-sample data has a probability of having any heart attack or not. This model can help in decision making along with the doctor to treat the patient well and creating a transparency between the doctor and the patient. In the validation set of the data, it’s not only the accuracy that the model has to take care, rather the True Positive Rate and False-Negative Rate along with the AUC-ROC helps in building/fixing the algorithm inside the model.
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
"The proposed system overcomes the above mentioned issue in an efficient way. It aims at analyzing the number of fraud transactions that are present in the dataset.
"
This paper helps in foreseeing diabetes by applying data mining strategy. The revelation of information
from clinical datasets is significant so as to make powerful medical determination. The point of data mining is to
extricate information from data put away in dataset and produce clear and reasonable depiction of examples. Diabetes
is an interminable sickness and a significant general wellbeing challenge around the world. Utilizing data mining
techniques by taking hba1c test data to help individuals to predict diabetes has increase significant fame. In this paper,
six classification models are used to classify a diabetic or non-diabetic patient and male and female patients. The
dataset utilized is gathered from a Diagnostics and research laboratory Liaquat university of medical and health
sciences Jamshoro, which gathers the data of patients with diabetes, without diabetes by taking blood sample of patient
and performing hba1c. We utilized Weka tool for the analysis diabetes, no-diabetic examination. Out of six
classification algorithms, four algorithms depict hundred percent accuracy on train and test data.
KEY WORDS: Data mining, Diabetes, HbA1c, Classification models, Weka.
Diabetes Prediction Using Machine Learningjagan477830
Our proposed system aims at Predicting the number of Diabetes patients and eliminating the risk of False Negatives Drastically.
In proposed System, we use Random forest, Decision tree, Logistic Regression and Gradient Boosting Classifier to classify the Patients who are affected with Diabetes or not.
Random Forest and Decision Tree are the algorithms which can be used for both classification and regression.
The dataset is classified into trained and test dataset where the data can be trained individually, these algorithms are very easy to implement as well as very efficient in producing better results and can able to process large amount of data.
Even for large dataset these algorithms are extremely fast and can able to give accuracy of about over 90%.
Diabetes is a disease which is rapidly increasing all over the world. It occurs when pancreas does not produce sufficient insulin, or body can not sufficiently use insulin it produces. Diabetes person has increase blood glucose in the body. One of the major problem diabetic patients suffers from is the Diabetic Retinopathy (DR) and blindness. Since the number of diabetes patients is continuously increasing, it increases the data as well.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical
research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more
number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well
as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
Heart is the most important organ of a human body. It circulates oxygen and other vital nutrients through blood to different parts of the body and helps in the metabolic activities. Apart from this it also helps in removal of the metabolic wastes. Thus, even minor problems in heart can affect the whole organism. Researchers are diverting a lot of data analysis work for assisting the doctors to predict the heart problem. So, an analysis of the data related to different health problems and its functioning can help in predicting with a certain probability for the wellness of this organ. In this paper we have analysed the different prescribed data of 1094 patients from different parts of India. Using this data, we have built a model which gets trained using this data and tries to predict whether a new out-of-sample data has a probability of having any heart attack or not. This model can help in decision making along with the doctor to treat the patient well and creating a transparency between the doctor and the patient. In the validation set of the data, it’s not only the accuracy that the model has to take care, rather the True Positive Rate and False-Negative Rate along with the AUC-ROC helps in building/fixing the algorithm inside the model.
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
"The proposed system overcomes the above mentioned issue in an efficient way. It aims at analyzing the number of fraud transactions that are present in the dataset.
"
This paper helps in foreseeing diabetes by applying data mining strategy. The revelation of information
from clinical datasets is significant so as to make powerful medical determination. The point of data mining is to
extricate information from data put away in dataset and produce clear and reasonable depiction of examples. Diabetes
is an interminable sickness and a significant general wellbeing challenge around the world. Utilizing data mining
techniques by taking hba1c test data to help individuals to predict diabetes has increase significant fame. In this paper,
six classification models are used to classify a diabetic or non-diabetic patient and male and female patients. The
dataset utilized is gathered from a Diagnostics and research laboratory Liaquat university of medical and health
sciences Jamshoro, which gathers the data of patients with diabetes, without diabetes by taking blood sample of patient
and performing hba1c. We utilized Weka tool for the analysis diabetes, no-diabetic examination. Out of six
classification algorithms, four algorithms depict hundred percent accuracy on train and test data.
KEY WORDS: Data mining, Diabetes, HbA1c, Classification models, Weka.
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
Machine learning approach for predicting heart and diabetes diseases using da...IAESIJAI
Environmental changes and food habits affect people's health with numerous diseases in today's life. Machine learning is a technique that plays a vital role in predicting diseases from collected data. The health sector has plenty of electronic medical data, which helps this technique to diagnose various diseases quickly and accurately. There has been an improvement in accuracy in medical data analysis as data continues to grow in the medical field. Doctors may have a hard time predicting symptoms accurately. This proposed work utilized Kaggle data to predict and diagnose heart and diabetic diseases. The diseases heart and diabetes are the foremost cause of higher death rates for people. The dataset contains target features for the diagnosis of heart disease. This work finds the target variable for diabetic disease by comparing the patient's blood sugars to normal levels. Blood pressure, body mass index (BMI), and other factors diagnose these diseases and disorders. This work justifies the filter method and principal component analysis for selecting and extracting the feature. The main aim of this work is to highlight the implementation of three ensemble techniques-Adaptive boost, Extreme Gradient boosting, and Gradient boosting-as well as the emphasis placed on the accuracy of the results.
Anemia is a state of poor health where there is presence of low amount of red blood cell in blood stream.
This research aims to design a model for prediction of Anemia in children under 5 years of age using
Complete Blood Count reports. Data are collected from Kanti Children Hospital which consist of 700 data
records. Then they are preprocessed, normalized, balanced and selected machine learning algorithms were
applied. It is followed by verification, validation along with result analysis. Random Forest is the best
performer which showed accuracy of 98.4%. Finally, Feature Selection as well as Ensemble Learning
methods, Voting, Stacking, Bagging and Boosting were applied to improve the performance of algorithms.
Selecting the best performer algorithm, stacking with other algorithms, bagging it, boosting it are very
much crucial to improve accuracy despite of any time issue for prediction of anemia in children below 5
years of age.
Anemia is a state of poor health where there is presence of low amount of red blood cell in blood stream.
This research aims to design a model for prediction of Anemia in children under 5 years of age using
Complete Blood Count reports. Data are collected from Kanti Children Hospital which consist of 700 data
records. Then they are preprocessed, normalized, balanced and selected machine learning algorithms were
applied. It is followed by verification, validation along with result analysis. Random Forest is the best
performer which showed accuracy of 98.4%. Finally,
A CONCEPTUAL APPROACH TO ENHANCE PREDICTION OF DIABETES USING ALTERNATE FEATU...IAEMEPublication
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.
A CONCEPTUAL APPROACH TO ENHANCE PREDICTION OF DIABETES USING ALTERNATE FEATU...IAEME Publication
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.
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
AN IMPROVED MODEL FOR CLINICAL DECISION SUPPORT SYSTEMijaia
Misguided information in health care has caused much havoc that have led to the death of millions of people as a result of misclassification, and inconsistent health care records; hence the objective of this paper is to develop an improved clinical decision support system. This system incorporated hybrid system
of non-knowledge based and knowledge based decision support system for the diagnosis of diseases and proper health care delivery records using prostate cancer and diabetes datasets to train and validate the model. The min-max method was adopted in normalizing the datasets, while genetic algorithm was
deployed in initiating the training weights of the MLP. The result obtained in this paper yielded a classification accuracy of 98%, sensitivity of 0.98 and specificity of 100 for prostate cancer and accuracy of 94%, sensitivity of 0.94 and specificity of 0.67 for diabetes.
DIAGNOSIS OF OBESITY LEVEL BASED ON BAGGING ENSEMBLE CLASSIFIER AND FEATURE S...ijaia
In the current era, the amount of data generated from various device sources and business transactions is
rising exponentially, and the current machine learning techniques are not feasible for handling the massive
volume of data. Two commonly adopted schemes exist to solve such issues scaling up the data mining
algorithms and data reduction. Scaling the data mining algorithms is not the best way, but data reduction
is feasible. There are two approaches to reducing datasets selecting an optimal subset of features from the
initial dataset or eliminating those that contribute less information. Overweight and obesity are increasing
worldwide, and forecasting future overweight or obesity could help intervention. Our primary objective is
to find the optimal subset of features to diagnose obesity. This article proposes adapting a bagging
algorithm based on filter-based feature selection to improve the prediction accuracy of obesity with a
minimal number of feature subsets. We utilized several machine learning algorithms for classifying the
obesity classes and several filter feature selection methods to maximize the classifier accuracy. Based on
the results of experiments, Pairwise Consistency and Pairwise Correlation techniques are shown to be
promising tools for feature selection in respect of the quality of obtained feature subset and computation
efficiency. Analyzing the results obtained from the original and modified datasets has improved the
classification accuracy and established a relationship between obesity/overweight and common risk factors
such as weight, age, and physical activity patterns.
Submission Deadline: 30th September 2022
Acceptance Notification: Within Three Days’ time period
Online Publication: Within 24 Hrs. time Period
Expected Date of Dispatch of Printed Journal: 5th October 2022
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...IAEME Publication
White layer thickness (WLT) formed and surface roughness in wire electric discharge turning (WEDT) of tungsten carbide composite has been made to model through response surface methodology (RSM). A Taguchi’s standard Design of experiments involving five input variables with three levels has been employed to establish a mathematical model between input parameters and responses. Percentage of cobalt content, spindle speed, Pulse on-time, wire feed and pulse off-time were changed during the experimental tests based on the Taguchi’s orthogonal array L27 (3^13). Analysis of variance (ANOVA) revealed that the mathematical models obtained can adequately describe performance within the parameters of the factors considered. There was a good agreement between the experimental and predicted values in this study.
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURSIAEME Publication
The study explores the reasons for a transgender to become entrepreneurs. In this study transgender entrepreneur was taken as independent variable and reasons to become as dependent variable. Data were collected through a structured questionnaire containing a five point Likert Scale. The study examined the data of 30 transgender entrepreneurs in Salem Municipal Corporation of Tamil Nadu State, India. Simple Random sampling technique was used. Garrett Ranking Technique (Percentile Position, Mean Scores) was used as the analysis for the present study to identify the top 13 stimulus factors for establishment of trans entrepreneurial venture. Economic advancement of a nation is governed upon the upshot of a resolute entrepreneurial doings. The conception of entrepreneurship has stretched and materialized to the socially deflated uncharted sections of transgender community. Presently transgenders have smashed their stereotypes and are making recent headlines of achievements in various fields of our Indian society. The trans-community is gradually being observed in a new light and has been trying to achieve prospective growth in entrepreneurship. The findings of the research revealed that the optimistic changes are taking place to change affirmative societal outlook of the transgender for entrepreneurial ventureship. It also laid emphasis on other transgenders to renovate their traditional living. The paper also highlights that legislators, supervisory body should endorse an impartial canons and reforms in Tamil Nadu Transgender Welfare Board Association.
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURSIAEME Publication
Since ages gender difference is always a debatable theme whether caused by nature, evolution or environment. The birth of a transgender is dreadful not only for the child but also for their parents. The pain of living in the wrong physique and treated as second class victimized citizen is outrageous and fully harboured with vicious baseless negative scruples. For so long, social exclusion had perpetuated inequality and deprivation experiencing ingrained malign stigma and besieged victims of crime or violence across their life spans. They are pushed into the murky way of life with a source of eternal disgust, bereft sexual potency and perennial fear. Although they are highly visible but very little is known about them. The common public needs to comprehend the ravaged arrogance on these insensitive souls and assist in integrating them into the mainstream by offering equal opportunity, treat with humanity and respect their dignity. Entrepreneurship in the current age is endorsing the gender fairness movement. Unstable careers and economic inadequacy had inclined one of the gender variant people called Transgender to become entrepreneurs. These tiny budding entrepreneurs resulted in economic transition by means of employment, free from the clutches of stereotype jobs, raised standard of living and handful of financial empowerment. Besides all these inhibitions, they were able to witness a platform for skill set development that ignited them to enter into entrepreneurial domain. This paper epitomizes skill sets involved in trans-entrepreneurs of Thoothukudi Municipal Corporation of Tamil Nadu State and is a groundbreaking determination to sightsee various skills incorporated and the impact on entrepreneurship.
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONSIAEME Publication
The banking and financial services industries are experiencing increased technology penetration. Among them, the banking industry has made technological advancements to better serve the general populace. The economy focused on transforming the banking sector's system into a cashless, paperless, and faceless one. The researcher wants to evaluate the user's intention for utilising a mobile banking application. The study also examines the variables affecting the user's behaviour intention when selecting specific applications for financial transactions. The researcher employed a well-structured questionnaire and a descriptive study methodology to gather the respondents' primary data utilising the snowball sampling technique. The study includes variables like performance expectations, effort expectations, social impact, enabling circumstances, and perceived risk. Each of the aforementioned variables has a major impact on how users utilise mobile banking applications. The outcome will assist the service provider in comprehending the user's history with mobile banking applications.
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONSIAEME Publication
Technology upgradation in banking sector took the economy to view that payment mode towards online transactions using mobile applications. This system enabled connectivity between banks, Merchant and user in a convenient mode. there are various applications used for online transactions such as Google pay, Paytm, freecharge, mobikiwi, oxygen, phonepe and so on and it also includes mobile banking applications. The study aimed at evaluating the predilection of the user in adopting digital transaction. The study is descriptive in nature. The researcher used random sample techniques to collect the data. The findings reveal that mobile applications differ with the quality of service rendered by Gpay and Phonepe. The researcher suggest the Phonepe application should focus on implementing the application should be user friendly interface and Gpay on motivating the users to feel the importance of request for money and modes of payments in the application.
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINOIAEME Publication
The prototype of a voice-based ATM for visually impaired using Arduino is to help people who are blind. This uses RFID cards which contain users fingerprint encrypted on it and interacts with the users through voice commands. ATM operates when sensor detects the presence of one person in the cabin. After scanning the RFID card, it will ask to select the mode like –normal or blind. User can select the respective mode through voice input, if blind mode is selected the balance check or cash withdraw can be done through voice input. Normal mode procedure is same as the existing ATM.
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...IAEME Publication
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends for conflict management as an integral part of effective human resource management.
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMYIAEME Publication
Our life journey, in general, is closely defined by the way we understand the meaning of why we coexist and deal with its challenges. As we develop the "inspiration economy", we could say that nearly all of the challenges we have faced are opportunities that help us to discover the rest of our journey. In this note paper, we explore how being faced with the opportunity of being a close carer for an aging parent with dementia brought intangible discoveries that changed our insight of the meaning of the rest of our life journey.
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...IAEME Publication
The main objective of this study is to analyze the impact of aspects of Organizational Culture on the Effectiveness of the Performance Management System (PMS) in the Health Care Organization at Thanjavur. Organizational Culture and PMS play a crucial role in present-day organizations in achieving their objectives. PMS needs employees’ cooperation to achieve its intended objectives. Employees' cooperation depends upon the organization’s culture. The present study uses exploratory research to examine the relationship between the Organization's culture and the Effectiveness of the Performance Management System. The study uses a Structured Questionnaire to collect the primary data. For this study, Thirty-six non-clinical employees were selected from twelve randomly selected Health Care organizations at Thanjavur. Thirty-two fully completed questionnaires were received.
Living in 21st century in itself reminds all of us the necessity of police and its administration. As more and more we are entering into the modern society and culture, the more we require the services of the so called ‘Khaki Worthy’ men i.e., the police personnel. Whether we talk of Indian police or the other nation’s police, they all have the same recognition as they have in India. But as already mentioned, their services and requirements are different after the like 26th November, 2008 incidents, where they without saving their own lives has sacrificed themselves without any hitch and without caring about their respective family members and wards. In other words, they are like our heroes and mentors who can guide us from the darkness of fear, militancy, corruption and other dark sides of life and so on. Now the question arises, if Gandhi would have been alive today, what would have been his reaction/opinion to the police and its functioning? Would he have some thing different in his mind now what he had been in his mind before the partition or would he be going to start some Satyagraha in the form of some improvement in the functioning of the police administration? Really these questions or rather night mares can come to any one’s mind, when there is too much confusion is prevailing in our minds, when there is too much corruption in the society and when the polices working is also in the questioning because of one or the other case throughout the India. It is matter of great concern that we have to thing over our administration and our practical approach because the police personals are also like us, they are part and parcel of our society and among one of us, so why we all are pin pointing towards them.
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...IAEME Publication
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INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...IAEME Publication
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A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...IAEME Publication
Banking regulations act of India, 1949 defines banking as “acceptance of deposits for the purpose of lending or investment from the public, repayment on demand or otherwise and withdrawable through cheques, drafts order or otherwise”, the major participants of the Indian financial system are commercial banks, the financial institution encompassing term lending institutions. Investments institutions, specialized financial institution and the state level development banks, non banking financial companies (NBFC) and other market intermediaries such has the stock brokers and money lenders are among the oldest of the certain variants of NBFC and the oldest market participants. The asset quality of banks is one of the most important indicators of their financial health. The Indian banking sector has been facing severe problems of increasing Non- Performing Assets (NPAs). The NPAs growth directly and indirectly affects the quality of assets and profitability of banks. It also shows the efficiency of banks credit risk management and the recovery effectiveness. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets that why is a double edge weapon. This paper outlines the concept of quality of bank loans of different types like Housing, Agriculture and MSME loans in state Haryana of selected public and private sector banks. This study is highlighting problems associated with the role of commercial bank in financing Small and Medium Scale Enterprises (SME). The overall objective of the research was to assess the effect of the financing provisions existing for the setting up and operations of MSMEs in the country and to generate recommendations for more robust financing mechanisms for successful operation of the MSMEs, in turn understanding the impact of MSME loans on financial institutions due to NPA. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of selected public sector and private sector banks and attempts to compare the NPA of Housing, Agriculture and MSME loans in state Haryana of public and private sector banks. The tools used in the study are average and Anova test and variance. The findings reveal that NPA is common problem for both public and private sector banks and is associated with all types of loans either that is housing loans, agriculture loans and loans to SMES. NPAs of both public and private sector banks show the increasing trend. In 2010-11 GNPA of public and private sector were at same level it was 2% but after 2010-11 it increased in many fold and at present there is GNPA in some more than 15%. It shows the dark area of Indian banking sector.
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...IAEME Publication
An experiment conducted in this study found that BaSO4 changed Nylon 6's mechanical properties. By changing the weight ratios, BaSO4 was used to make Nylon 6. This Researcher looked into how hard Nylon-6/BaSO4 composites are and how well they wear. Experiments were done based on Taguchi design L9. Nylon-6/BaSO4 composites can be tested for their hardness number using a Rockwell hardness testing apparatus. On Nylon/BaSO4, the wear behavior was measured by a wear monitor, pinon-disc friction by varying reinforcement, sliding speed, and sliding distance, and the microstructure of the crack surfaces was observed by SEM. This study provides significant contributions to ultimate strength by increasing BaSO4 content up to 16% in the composites, and sliding speed contributes 72.45% to the wear rate
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...IAEME Publication
The majority of the population in India lives in villages. The village is the back bone of the country. Village or rural industries play an important role in the national economy, particularly in the rural development. Developing the rural economy is one of the key indicators towards a country’s success. Whether it be the need to look after the welfare of the farmers or invest in rural infrastructure, Governments have to ensure that rural development isn’t compromised. The economic development of our country largely depends on the progress of rural areas and the standard of living of rural masses. Village or rural industries play an important role in the national economy, particularly in the rural development. Rural entrepreneurship is based on stimulating local entrepreneurial talent and the subsequent growth of indigenous enterprises. It recognizes opportunity in the rural areas and accelerates a unique blend of resources either inside or outside of agriculture. Rural entrepreneurship brings an economic value to the rural sector by creating new methods of production, new markets, new products and generate employment opportunities thereby ensuring continuous rural development. Social Entrepreneurship has the direct and primary objective of serving the society along with the earning profits. So, social entrepreneurship is different from the economic entrepreneurship as its basic objective is not to earn profits but for providing innovative solutions to meet the society needs which are not taken care by majority of the entrepreneurs as they are in the business for profit making as a sole objective. So, the Social Entrepreneurs have the huge growth potential particularly in the developing countries like India where we have huge societal disparities in terms of the financial positions of the population. Still 22 percent of the Indian population is below the poverty line and also there is disparity among the rural & urban population in terms of families living under BPL. 25.7 percent of the rural population & 13.7 percent of the urban population is under BPL which clearly shows the disparity of the poor people in the rural and urban areas. The need to develop social entrepreneurship in agriculture is dictated by a large number of social problems. Such problems include low living standards, unemployment, and social tension. The reasons that led to the emergence of the practice of social entrepreneurship are the above factors. The research problem lays upon disclosing the importance of role of social entrepreneurship in rural development of India. The paper the tendencies of social entrepreneurship in India, to present successful examples of such business for providing recommendations how to improve situation in rural areas in terms of social entrepreneurship development. Indian government has made some steps towards development of social enterprises, social entrepreneurship, and social in- novation, but a lot remains to be improved.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...IAEME Publication
Manufacturing industries have witnessed an outburst in productivity. For productivity improvement manufacturing industries are taking various initiatives by using lean tools and techniques. However, in different manufacturing industries, frugal approach is applied in product design and services as a tool for improvement. Frugal approach contributed to prove less is more and seems indirectly contributing to improve productivity. Hence, there is need to understand status of frugal approach application in manufacturing industries. All manufacturing industries are trying hard and putting continuous efforts for competitive existence. For productivity improvements, manufacturing industries are coming up with different effective and efficient solutions in manufacturing processes and operations. To overcome current challenges, manufacturing industries have started using frugal approach in product design and services. For this study, methodology adopted with both primary and secondary sources of data. For primary source interview and observation technique is used and for secondary source review has done based on available literatures in website, printed magazines, manual etc. An attempt has made for understanding application of frugal approach with the study of manufacturing industry project. Manufacturing industry selected for this project study is Mahindra and Mahindra Ltd. This paper will help researcher to find the connections between the two concepts productivity improvement and frugal approach. This paper will help to understand significance of frugal approach for productivity improvement in manufacturing industry. This will also help to understand current scenario of frugal approach in manufacturing industry. In manufacturing industries various process are involved to deliver the final product. In the process of converting input in to output through manufacturing process productivity plays very critical role. Hence this study will help to evolve status of frugal approach in productivity improvement programme. The notion of frugal can be viewed as an approach towards productivity improvement in manufacturing industries.
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENTIAEME Publication
In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
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In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
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Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
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In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
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2. Temesgen Abera Asfaw
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imaginably independent causal factor to micro-vascular entanglements. Diabetic patients are
maybe more incapable against a hoisted risk of micro-vascular damage, in this way long term
difficulty of cardio-vascular disease is the leading reason of death. This micro-vascular harm
and hasty cardio vascular disease ultimately quick to retinopathy, nephropathy and
neuropathy [3].Early prediction of such disease can be controlled over the diseases and save
human life. To accomplish this goal, this research work mainly discovers the early prediction
of diabetes by taking into account various risk factors related to this disease. For the willpower
of the study we gathered diagnostic dataset having 16 attributes diabetic of 2000 patients.
These attributes are age, diet, hyper-tension, problem in vision, genetic etc. In later part, we
debate about these attributes with their conforming values. Based on these attributes, we
figure prediction model by means of various machine learning techniques to predict diabetes
mellitus. Machine learning techniques provide well-organized result to extract knowledge by
making predicting models from diagnostic medical datasets composed from the diabetic
patients. Haul out knowledge from such data can be beneficial to predict diabetic patients.
Innumerable machine learning techniques have the knack to predict diabetes mellitus. Though
it is very difficult to select the best technique to predict based on such attributes. Thus for the
determination of the study, we deal six popular machine learning algorithms, namely Support
Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN) ,Logistic Regression
(LR) ,Random Forest (RF) and C4.5 decision tree (DT), on adult population data to predict
diabetic mellitus.
2. RELATED WORK
Various researchers have been shown revisions in the area of diabetic by using machine
learning techniques to extract knowledge from existing medical data. For illustration,
ALjumah et. al. [4] established a predictive analysis model using support vector machine
algorithm. In [5], Kavakiotis et al. used 10 fold cross validation as evaluation method in three
different algorithms, including Logistic regression, Naive Bayes, and SVM, where SVM on
condition that better performance and accuracy of 84 % than other algorithms. In [6], Zheng
et al. applied Random Forest, KNN, Naive Bayes, SVM, decision tree and logistic regression
to predict diabetes mellitus at early stage, where cleaning criteria can be improved. Swarupa
et al. [7] applied J48, ANN, KNN, ZeroR and NB on various diabetes dataset. Pradeep et al.
[8] applied Random Forest, KNN, SVM and J48 where J48 shows better performance than
others. The classification algorithms did not assess using cross validation method. To predict
and control diabetes mellitus Huang et al [9] conversed three data mining methods, including
IB1, Naïve Bayes and C4.5 in the year of 2000 to 2004. By smearing feature selection
technique, the performance of IB1 and Naive Bayes provided better result. In [10], Xue-Hui
Meng et al. used three different data mining techniques ANN, Logistic regression, and J48 to
predict the diabetic diseases using real world data sets. Finally it was concluded as J48
performs better accuracy than others. In this work, we examine real diagnostic medical data
based on numerous risk factors using popular machine learning classification techniques to
assess their performance for predicting diabetes mellitus.
3. METHODOLOGY
In order to accomplish our goal study methodology includes of few stages, which are accrual
of diabetes dataset with the applicable attributes of the patients, preprocessing the numeric
value attributes, to smear several machine learning techniques and conforming predictive
analysis employing such data. In the subsequent, we fleetingly confer these stages.
3. Prediction of Diabetes Mellitus Using Machine Learning Techniques
http://www.iaeme.com/IJCET/index.asp 27 editor@iaeme.com
3.1. Dataset and Attributes
In this work, datasets have been collected among the Pima Indian female population near
Phoenix, Arizona. This particular dataset has been widely used in machine learning
experiments and is currently available through the UCI repository of standard datasets. This
population has been studied continuously by the National Institute of Diabetes, Digestive and
Kidney. UCI repository contains 768 instances of observations and total 9 attributes with no
missing values reported. Data sets contains 8 particular variables which were considered high
risk factors for the occurrence of diabetes, like number of times pregnant, plasma glucose
concentration at 2 hour in an oral glucose tolerance test (OGTT), diastolic blood pressure, 2
hour serum insulin, body mass index, diabetes pedigree. All the patients in this datasets are
female at least 21 years old living near Phoenix, Arizona. All attributes are numeric values
except class is nominal type. Attributes name and types are shown in table 1.
Table 1 Dataset Description
No Name of attributes Type
1 Number of times pregnant Numeric
2 Plasma glucose concentration a 2 hours in an oral
glucose tolerance test Numeric
Numeric
3 Diastolic blood pressure Numeric
4 Triceps skin fold thickness Numeric
5 2 hour serum insulin Numeric
6 Body mass index Numeric
7 Diabetes pedigree function Numeric
Figure 1 Sample dataset
It is good to check the correlations between the attributes. From the output graph below,
the red around the diagonal suggests that attributes are correlated with each other. The yellow
and green patches suggest some moderate correlation and the blue boxes show negative
correlations as shown below fig. 2.
4. Temesgen Abera Asfaw
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Figure 2 Attributes Correlation
Next, we visualize the data using density plots to get a sense of the data distribution. From
the outputs below, you can see the data shows a general Gaussian distribution below fig 3.
Figure 3 Density plots
Figure 4 Histogram
5. Prediction of Diabetes Mellitus Using Machine Learning Techniques
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According to Fig. 5 below, after defining the problem we collect the relevant data from
the Diagnostic Data Storage. We then preprocess the data for the purpose of building the
prediction model. After that we apply various machine learning techniques discussed above
on the training dataset. Finally, test dataset is used to measure the performance of the
techniques in order to choose the best classifier for predicting diabetes mellitus.
Figure 5 Shows an overview of the overall process of our work.
3.2. Apply Machine Learning Techniques
After the data has been ready for demonstrating, we use six popular machine learning
techniques to predict diabetes mellitus. Later we offer an overview of these techniques.
Support Vector Machines: is widely classification technique proposed by J. Platt et. al. [11].
A Support Vector Machine is known by characterizing the data by separating a hyper plane.
SVM detaches entities in specified classes. It can also recognize and classify instances which
are not supported by data. SVM is not caring in the distribution of acquiring data of each
class. The one delay of this algorithm is to execute regression analysis to produce a linear
function and another extension is learning to rank elements to yield classification for
individual elements.
Naive Bayes: is a popular probabilistic classification technique proposed by John et. al. [12].
Naive Bayes also called Bayesian theorem is a simple, effective and commonly used machine
learning classifier. The algorithm calculates probabilistic results by counting the regularity
and combines the value given in data set. By using Bayesian theorem, it adopts that all
attributes are independent and based on variable values of classes. In real world solicitation,
the conditional independence hypothesis rarely holds true and gives well and more
sophisticate classifier results.
K-Nearest Neighbor Algorithm: K-nearest neighbor is simple regression and classification
algorithm that used non parametric method proposed by Aha et. al. [13]. The algorithm trains
all usable attributes and classifies new query based on their likeness measure. To determine
the distance from point of interest to points in training data set it uses tree like data structure.
The attribute is classified by its neighbors.
6. Temesgen Abera Asfaw
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Decision Tree: is a tree that provides powerful classification techniques to predict diabetes
mellitus. Every discrete area and feature of the domain is called a class. An input feature of
the class attribute is labeled with the internal node in a tree. The leaf node of the tree is
labeled by attribute and each attribute associated with a target value. There are some popular
decision tree algorithms are accessible to classify diabetic data in machine learning
techniques, including ID3, C4.5 , J48, C5, CART and CHAID. C4.5 provides extended
features of ID3 decision tree algorithm proposed by Ross Quinlan et. al. [14]. C4.5 decision
tree uses same training data as ID3, in which learned function is introduced. The learning
method can be used to diagnose medical data to predict the value of the decision attribute.
Logistic regression: is a probabilistic statistical model for investigative a dataset in which
there are one or more independent variables that govern a result. In logistic regression, the
dependent variable is binary that means 1 as (TRUE, patient, etc.) or 0 as (FALSE, healthy,
etc.). Logistic regression produces the coefficients of a procedure to predict a log it
adjustment of the probability of occurrence of the characteristic of nosiness.
Random forest: is collaborative classification scheme based on Decision Tree. At the
training stage, it produces a massive number of trees and creates a forest of Decision Trees.
At the testing stage, each tree of the forest predicts a class label for each data. When each tree
predicts a class label, then the final decision for each test data depends on widely held voting.
Which class label gets the majority of votes this label accepts to be the correct label allotted to
the test data. This process is continual for each of data in the dataset.
4. EXPERIMENTAL RESULTS AND DISCUSSION
To conduct the experiment 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 were used. Machine
learning techniques were implemented in pycharm 3.6. An experimental result shows that the
performance of SVM is significantly superior to other machine learning techniques for the
classification of diabetic data. The experimental results could assist health care to take early
prevention and make better clinical decisions to control diabetes and thus save human life. To
take into account additional attributes and evaluation for further analysis is our future work.
Figure 6 Comparison result of Various Machine Learning Techniques
7. Prediction of Diabetes Mellitus Using Machine Learning Techniques
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In order to assess the performance of various machine learning techniques, we have
showed the prediction results in Fig. 6 on the basis of accuracy. The figure shows the results
of studies such as LR, SVM, NB, KNN, RF and C4.5 and SVM accomplishes better results
than other classifiers to predict diabetes mellitus. According to Fig. 6, SVM achieves 96.4%
on this dataset, which is greater than other learning techniques. This experimental result
provides indication that SVM performs acceptable on medical datasets for the determination
of predicting diabetes mellitus based on numerous risk factors, deliberated in the earlier
section.
Table 2 Accuracy Results of Various Machine Learning Techniques
Classification Algorithms Accuracy
Support Vector Machine (SVM) 96.4%
Decision Tree (DT) 95.8%
Random Forest (RF) 95.3%
Naïve Bayes (NB) 76.0%
Logistic Regression (LR) 77.6%
K-Nearest Neighbors ( KNN) 78.8%
Overall, we have selected the best machine learning technique to predict diabetes mellitus
to achieve high performance, based on the evaluation criteria discuss above. All the
techniques mentioned over are estimated on an unseen testing diabetic dataset. The technique
which accomplishes the highest performance in terms of accuracy is considered to be the best
choice. Based on Fig. 6, it can be observed that SVM achieved the better accuracy of 96.4 %
to predict diabetes mellitus utilizing a given medical dataset.
5. CONCLUSION
In this work, we have investigated the early prediction of diabetes by taking into account
several risk factors related to this disease using machine learning techniques To predict
diabetes mellitus efficiently, we have done our investigation using six popular machine
learning algorithms, namely Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest
Neighbor (KNN), Logistic Regression (LR.), Random Forest (RF.) and C4.5 decision tree, on
adult population data to predict diabetes mellitus. The technique which accomplishes the
highest performance in terms of accuracy is considered to be the best choice. Based on Fig.6,
it can be observed that SVM achieved the better accuracy of 96.4 % to predict diabetes
mellitus utilizing a given medical dataset.
REFERENCES
[1] V., A. K. and R., C. 2013. Classification of Diabetes Disease Using Support Vector
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