Diabetes Prediction Using Machine Learning Project
We utilize machine learning methods to forecast diabetes is a broad researched area within healthcare and medicine. For best management and the disease treatment are caused by forecasting diabetes that aids in early diagnosis. We work in a different framework so we fill the preliminary research gap by formulating correct solution. Top-tier Diabetes Prediction research proposal are given by our expert writers, more over our service extends with proof reading and paper work editing.
To construct a diabetes prediction project by utilizing machine learning techniques, here we give a step-by-step guidance:
- Define Your Objective:
- We forecast the diabetes in a person, and the chances of increase in diabetes on the basis of definite diagnostic measurements.
- Data Collection:
- Public Datasets: Our work extensively utilizes the dataset for diabetes forecasting, are UCI Machine Learning Repository that contains Pima Indians Diabetes Database.
- Data Preprocessing:
- Handling Missing Values: In our work, we have the requirement of assigned or dropped, since some of the datasets have missing values.
- Normalization/Standardization: By utilizing Min-Max scaling or Z- score normalization, we maintain the same scales for all features.
- Feature Selection: The most applicable features for forecasting are defined. Our work utilizes the approaches like correlation analysis, Recursive Feature Elimination (RFE), or utilizing frameworks like Random Forest for feature significance.
- Feature Extraction:
We extract new features on the basis of dataset and field knowledge that might be more informative. For example:
- Body Mass Index (BMI) from height and weight.
- Communication terms among features.
- Model Selection and Training:
- Splitting Data: Our work split the datasets into training and testing sets (e.g., an 80-20 split).
- Model Selection: Logistic Regression, Decision Trees, Random Forest, Gradient Boosting Machines, SVM, Neural Networks and K-Nearest Neighbors are the generally used frameworks for model selection tasks.
- Cross-Validation: To make sure that our framework generalizes well to unseen data, we utilize a method like k-fold cross-validation.
- Hyperparameter Tuning: We utilize approaches like grid search or random search to fine-tune the framework parameters for best achievement.
- Evaluation:
- Classification Metrics: To estimate our framework’s performance on a testing set, we utilize the metrics like Accuracy, Precision, Recall, F1-score, ROC curve and AUC.
- Confusion Matrix: To observe false positives and false negatives by a handy tool.
- Deployment:
- We establish it as a web service or combine it into medical software tools by satisfying the framework’s performance.
- In our work, we utilize our trained framework to make predictions in real-time by utilizing tools like FLASK or FastAPI in python that aid in producing web applications.
Project Extensions:
- Risk Stratifications: We categorize patients into various risk levels rather than a binary prediction (Diabetes or not diabetes).
- Explainability: SHAP or LIME is the tools that offer understandable feedback on forecasting and clarify that why a definite forecasting will be made.
- Time-Series Analysis: We forecast the advancement of the disease periodically, when we have longitudinal data.
Challenges:
- Imbalanced Data: In our work, if one class (e.g., non-diabetic) is much greater than the other, it causes biased forecasting.
- Data Privacy: To make sure that we have the essential approvals and obey security limitations, since the healthcare data is sensitive.
- Feature Interpretability: Healthcare specialists make sure that the structures and the framework decision are understood.
Recall that while machine learning can help in forecasting, the last diagnosis should always be produced by medical professionals. We make sure that any application we construct will emphasize this.
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Diabetes Prediction Using Machine Learning Project Thesis Ideas
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- Analysis of Diabetic Prediction Using Machine Learning Algorithms on BRFSS Dataset
Keywords:
Support vector machines, Heart, Machine learning algorithms, Sociology, Predictive models, Prediction algorithms, Diabetes
In this paper they investigate healthcare prediction analytics and address the issues using ML based methods. They utilized the early detection and binary 012 databases. Based on these datasets, the precision, recall, and accuracy of KNNs and Random Forest methods are calculated. SVM performs better and gives the better result.
- Data-Driven Diabetes Risk Factor Prediction Using Machine Learning Algorithms with Feature Selection Technique
Keywords:
Feature selection, risk factors
The purpose of this paper is to predict diabetes risk factors by applying ML algorithms. Two-fold feature selection techniques (i.e., principal component analysis and information gain IG) have been applied to boost the prediction accuracy. Then, the optimal features are fed to five ML algorithms namely decision tree, random forest, support vector machine, logistic regression, and KNN.
- Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning techniques
Keywords:
Gestational diabetes (GD), Clinical decision support system, Deep learning, Bayesian optimization, Random Forest
The study aimed to develop a clinical diagnosis system to identify patients in the GD risk group and reduce unnecessary oral glucose tolerance test (OGTT) applications for pregnant women who are not in the GD risk group using DL algorithms. As a result, a novel successful decision support model was developed using RNN-LSTM with Bayesian optimization that gave better outcome.
- HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System
Keywords:
Artificial Intelligence (AI); Cloud Computing; Diagnosis; Digital Health; Edge Computing; eHealth; Internet of Things (IoT); Logisitic Regression; Prognosis; Risk Factors; Smart Connected Healthcare; Type 2 Diabetes Mellitus
This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. The two most used ML algorithms are Random Forest (RF) and Logistic Regression (LR) using two real life diabetes datasets. The Random Forest gives the better outcomes compared to Logistic Regression.
- Impact of Nutritional Factors in Blood Glucose Prediction in Type 1 Diabetes Through Machine Learning
Keywords:
Glucose, Blood, Insulin, Predictive models, Neural networks, Patient monitoring, Pancreas
Type 1 Diabetes (T1D) is an autoimmune disease; a critical issue in T1D patients is the managing of Postprandial Glucose Response (PGR), through the dosing of the insulin bolus to inject before meals. The Artificial Pancreas (AP) will combine with insulin and blood glucose monitoring is a promising solution. A Feed-Forward Neural Network was fed with several dispositions of Blood Glucose Levels BGLs, insulin, and nutritional factors.
- Retinopathy prediction in type 2 diabetes: Time-varying Cox proportional hazards and machine learning models.
Keywords:
Diabetic retinopathy, electronic health record, Survival analysis, Time-to-event
Diabetic retinopathy (DR) is one of the most common complications in type 2 diabetes (T2D). Predictive modeling has been dependent on Cox proportional hazards (CPH) with assumptions of linearity and constant hazards. The CPH and ML models were compared using left-truncated right censoring relative risk forest (LTRC-RRF) and left-truncated right censoring conditional inference forest (LTRC-CIF) algorithms.
- Prediction Model of Type 2 Diabetes Mellitus for Oman Prediabetes Patients Using Artificial Neural Network and Six Machine Learning Classifiers
Keywords:
K-nearest neighbours (K-NN); naive Bayes (NB); decision tree; linear discriminant analysis (LDA); artificial neural network (ANN); type 2 diabetes mellitus (T2DM); Pima Indian Diabetes (PID) dataset.
This paper presents AI and ML prediction models for diagnosing type 2 diabetes mellitus (T2DM). Six machine learning algorithms: K-nearest neighbours (K-NN), support vector machine (SVM), naive Bayes (NB), decision tree, random forest (RF), linear discriminant analysis (LDA), and artificial neural networks (ANN) were applied. The random forest and decision tree models performed better than all the other algorithms.
- Prediction of Diabetes Mellitus Progression Using Supervised Machine Learning
Keywords:
Classification; prediction; dynamic plantar pressure; diabetic peripheral neuropathy; foot ulceration
Diabetic peripheral neuropathy (DN) is a serious complication of diabetes mellitus (DM). They present an approach for diagnosing various stages of the progression of DM in lower extremities using ML to classify individuals with prediabetes, diabetes without and diabetes with peripheral neuropathy based on dynamic pressure distribution collected using pressure-measuring insoles. Pressure data were grouped into three regions: rearfoot, midfoot, and forefoot.
- Diabetes Prediction in Teenagers using Machine Learning Algorithms
Keywords:
Radio frequency, Computational Modeling, Forestry, Prediction algorithms, Feature extraction
The aim of this paper is to develop a system that can predict diabetes in individuals aged 10to30 by merging the results of different ML algorithms. Some of the methods used to detect diabetes early are Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K Nearest Neighbours (KNN), and XGBoost. The RF and XGBoost gives better result.
- Towards Diabetes Mellitus Prediction Based on Machine- Learning
Keywords:
Pathology, Predictive analytics
Several machine-learning techniques were used for the predictive analysis of diabetes. They conduct a review of diabetes prediction and propose an approach for prediction of gestational diabetes using Deep Neural Network (DNN), Support Vector Machine (SVM), and Random Forest (RF) classifiers. The Random Forest gives the better outcome.
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