Predictive analysis of diabetic patient data using machine learning and Hadoop

Kalyankar, Gauri D.; Poojara, Shivananda R.; Dharwadkar, Nagaraj V.

doi:10.1109/i-smac.2017.8058253

Cited by 67 publications

(15 citation statements)

References 2 publications

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“…Reference [6] The Digital career prediction system is the use of statistical algorithms data, and machine learning techniques to identify the likelihood of future results from historical data, predictive models use known results to develop or train. A model that can be used to predict values for different or new data, (for example, in our system, the target variable is a student's success in courses based on the estimated importance from a set of input variables, which is different from the descriptive model that helps in understanding what happened or a diagnostic model that helps in understanding important relationships and determining why and what happened.…”

Section: Related Workmentioning

confidence: 99%

The Architecture of Intelligent Career Prediction System based on the Cognitive Technology for Producing Graduates to the Digital Manpower

Palee¹,

Wannapiroon²,

Nilsook³

2020

IJACSA

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Section: Related Workmentioning

confidence: 99%

The Architecture of Intelligent Career Prediction System based on the Cognitive Technology for Producing Graduates to the Digital Manpower

Palee¹,

Wannapiroon²,

Nilsook³

2020

IJACSA

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“…This is a Non-Communicable Disease (NCB), and numerous individuals are experiencing it. According to statistics in [2], from 2017, about 425 million individuals have diabetes. Diabetes causes between 2-5 million people to lose their lives each year.…”

Section: Introductionmentioning

confidence: 99%

Dm-Health App: Diabetes Diagnosis Using Machine Learning with Smartphone

Hossain¹,

Alshehri²,

Almakdi³

et al. 2022

Computers, Materials &Amp; Continua

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Diabetes Mellitus is one of the most severe diseases, and many studies have been conducted to anticipate diabetes. This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic, pre-diabetic, or non-diabetic without the assistance of any physician or medical tests. This study's methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design. The Diabetes Prediction Approach uses a novel approach, Light Gradient Boosting Machine (LightGBM), to ensure a faster diagnosis. The Proposed System Architecture Design has been combined into seven modules; the Answering Question Module is a natural language processing Chabot that can answer all kinds of questions related to diabetes. The Doctor Consultation Module ensures free treatment related to diabetes. In this research, 90% accuracy was obtained by performing K-fold cross-validation on top of the K nearest neighbor's algorithm (KNN) & LightGBM. To evaluate the model's performance, Receiver Operating Characteristics (ROC) Curve and Area under the ROC Curve (AUC) were applied with a value of 0.948 and 0.936, respectively. This manuscript presents some exploratory data analysis, including a correlation matrix and survey report. Moreover, the proposed solution can be adjustable in the daily activities of a diabetic patient.

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“…Different predictive analyses [1] include machine learning algorithms and statistical methods. Under this technique, the classification of past data is done to get knowledge to predict future events.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Technique to Prognosis Diabetes Disease: Random Forest Classifier Approach

Palimkar¹,

Shaw

Ghosh³

2021

Lecture Notes in Networks and Systems

112

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Diabetes is one among many chronic diseases. It is the most common disease and lots of peoples are affected by this. There are many things that are liable for diabetes, mainly age, obesity, weakness, sudden weight loss, and many more. Diabetes patients have high risk of diseases like cardiopathy, renal disorder, stroke, nerve damage, eye damage, etc. Detection of the disease isn't very easy and prediction is additionally costlier. In today's situation, hospitals are extremely busy due to COVID-19 pandemic, and it might be revolutionary if one could know if they're at risk of being diabetic without visiting a doctor. But the rise in Artificial Intelligence techniques can be used for disease prognosis. The objective of this study is to develop a model with significant accuracy to diagnose diabetes in patients. Moreover, this paper also presents an effective diabetes prediction model for better classification of diabetes and to enhance the accuracy in diabetes prediction using several machine learning algorithms. Different machine learning algorithms are utilized for early stage diabetes prediction, namely, Logistic Regression, Random Forest Classifier, Support Vector Machine, Decision Trees, K-Nearest Neighbors, Gaussian Process Classifier, AdaBoost Classifier, and Gaussian Naïve Bayes. The performances of these models are measured on respective criteria like Accuracy, Precision, Recall, F-Measure, and Error. For this research work, latest available dataset dated 22nd July, 2020, is being utilized. Latest updated dataset will show comparatively better result.

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Predictive analysis of diabetic patient data using machine learning and Hadoop

Cited by 67 publications

References 2 publications

The Architecture of Intelligent Career Prediction System based on the Cognitive Technology for Producing Graduates to the Digital Manpower

The Architecture of Intelligent Career Prediction System based on the Cognitive Technology for Producing Graduates to the Digital Manpower

Dm-Health App: Diabetes Diagnosis Using Machine Learning with Smartphone

Machine Learning Technique to Prognosis Diabetes Disease: Random Forest Classifier Approach

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