Our work utilizes Machine Learning methods to predict the kidney disease can support medical specialists in early diagnosis that is dangerous for effective management and treatment.
Have a keen look at our work get inspired and get our timely and high-quality research support services. Our team help you in Development of your synopsis, thesis, or research paper under Kidney Disease Prediction Using machine learning.
We have given a step-by-step guidance:
- Define the objective:
- We construct a model that predicts if the patient has kidney disease on the basis of set of medical characters.
- Data collection:
- Existing Datasets: Our work utilizes one of the commonly used datasets that is “chronic kidney disease” dataset that is accessible on the UCI Machine Learning Repository.
- Medical Records: The anonymized patient data can be offered from hospitals or health administrations (to make sure that you should follow the privacy regulations such as HIPAA).
- Data Preprocessing:
- Handle Missing Values: Sometimes the medical datasets contain missing values. We have to consider imputations or use methods that can handle missing values.
- Encoding: Changing categorical variables (e.g., red blood cell count: normal or abnormal) into numerical format by utilizing one-hot or label encoding.
- Normalization/Standardization: We have to normalize the data to maintain the same scale in all numerical attributes and our framework will execute efficiently.
- Feature Engineering:
- Domain Knowledge: To work together with the medical specialists we have to govern which characters are most suggestive to kidney disease. Some of the characteristic features we include are red blood cell count, blood pressure, glucose level, etc.
- Feature Selection: In our work we utilize some methods like recursive feature elimination, correlation matrix or domain knowledge to decrease dimensionality.
- Model Selection and Training:
- Model Selection: Initially we can start with some simple methods like Logistic Regression. Then we have slowly moved to more complex methods like Random Forest, SVM, or Neural Networks.
- Training: Our work split the dataset into training and validation sets. Then we utilize the training set for training and validation set for tuning the hyperparameters.
- Evaluation:
- Metrics: We utilize the metrics like Accuracy, Precision, Recall, F1 score. Provided that the medical characters, concentrate on recall (we do not want to loss positive cases) but to make sure that precision remains reasonable (to eliminate unnecessary medical interventions).
- ROC Curve and AUC: In our work we provide a complete view of the methods performance across various thresholds.
- Confusion Matrix: visualizing True Positives, False Positives, True Negatives and False Negatives are the Confusion matrix that can aid by us.
- Deployment:
- If the model achieves well then we have to consider organizing it as a tool for hospitals or clinics. To make sure that the executed solution can fulfills with the medical software regulations.
- Flask or FastAPI are the framework for web-based solutions that can utilized by us.
- Feedback and Iteration:
- We have to watch the model’s achievements continuously in a real-world clinical situation. Then we have to update the model as more data becomes available or if the performance drops.
Challenges:
- Data Privacy: since medical data is sensitive we make sure to have proper permissions and to obey rules.
- Imbalanced Data: often, the number of positive cases (people have kidney disease) is much lesser than negative cases. We have to consider the methods like oversampling, under sampling, or by utilizing synthetic data generation methods like SMOTE.
- Interpretability: In medical applications, our work understands why a model made prediction can be essential as the prediction itself. We have to consider by utilizing interpretable methods or tools like SHAP or LIME.
Extensions:
- Symptom checker: By lengthen the model our work includes some symptoms as their input features, that allows the patients to verify if their symptoms make a parallel with kidney disease.
- Treatment Recommendations: On the basis of severity of the disease, our work suggests possible treatments or further tests.
Work together with nephrologists or medical specialists they specializing kidney diseases with increasingly advantage to our project by ensure that it is clinically applicable and accurate.
phdprime.com covers almost every aspect of your research under machine learning that you may find hard to encounter with. We offer PhD research guidance in writing reliable and plagiarism-free research content which is clear and well understandable. We sophisticate it to such an extent that it becomes faultless, reasonable, and meaningful for your targeted readers.
Kidney Disease Prediction Using Machine Learning Research Thesis Topics
We bring you the required solution to get your machine learning thesis topic and title ideas. If you want a research topic or get a significant title for your research study, we guide you to make the right choice.
1. Chronic Kidney Disease Prediction using Machine Learning Models
Keywords
Chronic Kidney Disease, Decision Tree, Machine Learning, Random Forest, Support Vactor.
Implementation plan
Step 1: Initially we load the Chronic Disease data set.
Step 2: Next, perform the data pre-processing, which involves cleaning, extracting and transforming data to suitable formats.
Step 3: Next, perform the process of feature set selection and predict the early occurrence of chronic kidney disease by using three machine learning algorithms namely Decision tree, Random Forest and Support Vector machines.
Step 4: Next, calculate the Accuracy of the constructed classifier model.
Step 5: The performance of this work is measured through the following performance metrics, Accuracy, true positive, false positive, true negative, false negative, and time of execution.
Software Requirement: Python – 3.11.4 and Operating System: Windows 10(64-bit)
2. Statistical Analysis and Predicting Kidney Diseases using Machine Learning Algorithms
Keywords
Kidney, Statistical Analysis, Classifications, Data Mining, Machine Learning
Implementation plan
Step 1: Initially we load the kidney diseases data has been collected during 2014 and 2015 with 690 instances and 49 attributes.
Step 2: Next, the dataset has been analyzed using Weka tool.
Step 3: Next, clustering the data by using K-Means (KM) algorithm based on predefined k-value.
Step 4: Next, classify the data’s by using machine learning Classification algorithms, like Alternating Decision Tree (ADTree), Naive Bayes and Random Forest.
Step 5: Next, Visualize the kidney diseases detection details.
Step 6: The performance of these work is measured through the following performance metrics, Accuracy, ROC curve, Computation time and Confusion Matrix.
Software Requirement NetBeans-12.3, 8.9, 18 and JDK-20.0.2, and Operating System: Windows 10(64-bit)
- Prediction and Forecasting of Persistent Kidney Problems Using Machine Learning Algorithms
Keywords
Disease Forecasting, Kidney Diseases, Classification.
Implementation plan
Step 1: Initially we load the Chronic Kidney Disease Dataset, downloaded from the UCI Repository. This dataset contains 16 attributes (counting objective class characteristics) and 396 instances.
Step 2: Next, split the dataset into Training datasets and Test dataset and perform the data pre-processing, which involves cleaning and noise removal process.
Step 3: Next, perform the process of classification and predict the Persistent Kidney Problems by using proposed machine learning algorithm namely Stochastic Gradient Descent (SGD).
Step 4: Next, calculate the Accuracy of the classifier model.
Step 5: The performance of this work is measured through the following performance metrics, Accuracy, Precision, Recall, ROC curves and F-Score.
Software Requirement: Python – 3.11.4 and Operating System: Windows 10(64-bit)
- Prediction of Chronic Kidney Disease – A Machine Learning Perspective
Keywords
Chronic kidney disease, machine learning, prediction.
Implementation plan
Step 1: Initially Chronic Kidney Disease dataset has been taken from the UCI repository.
Step 2: Next, Perform the pre-processing for handle missing values, rescaling of the dataset, transform into binary data and standardize of the dataset
Step 3: Next, Perform the Feature selection process by using three different feature selection methods like, filter, Wrapper and embedded.
Step 4: Next, based on the selected Features perform the process of multi class classification and predict the chronic kidney disease by using linear support vector machine (LSVM).
Step 5: Finally, the performance of these work is measured through the following performance metrics, accuracy, precision, recall, F-measure, area under the curve and GINI coefficient.
Software Requirement: Python – 3.11.3 and Operating System: Windows 10(64-bit)
- Prediction of Chronic Kidney Disease Using Machine Learning Algorithm
Keywords
CKD, Decision Tree, GFR, SVM, Machine Learning
Implementation plan
Step 1: Initially we download and load the Dataset for prediction of chronic kidney disease from UCI repository. In that dataset there are 400 patient records are included.
Step 2: Next, perform the data pre-processing, for perform Data Cleaning and Data Reduction process.
Step 3: Next, split the pre-processed dataset into Training data and Testing data.
Step 4: Next, perform the classification by using machine learning algorithms Decision tree with J48 and Support Vector Machine.
Step 5: Next, perform the prediction process by using Decision tree and Support Vector Machine.
Step 6: The performance of this work is measured through the following performance metrics, Accuracy and time consuming.
Software Requirement: Python – 3.11.4 and Operating System: Windows 10(64-bit)
- Machine Learning Techniques for Chronic Kidney Disease Risk Prediction
Keywords
healthcare; chronic kidney disease; machine learning; prediction; data analysis
Implementation plan
Step 1: Initially the raw dataset consists of 400 instances represented by 13 input features and 1 for the target class.
Step 2: Next, perform the class balancing and features importance evaluation by using SMOTE and k-NN classifier.
Step 3: Next, perform the Features Analysis process based on Pearson correlation coefficient (CC), Gain Ratio (GR) and Random Forest.
Step 4: Next, construct the risk prediction framework for CKD occurrence by using Soft Voting.
Step 5: Finally, The performance of these work is measured through the following performance metrics, Area Under the Curve (AUC) , Precision, Recall, F-Measure and Accuracy.
Software Requirement: Python – 3.11.3 and Operating System: Windows 10(64-bit)
- Machine Learning Applied to Kidney Disease Prediction: Comparison Study
Keywords
Machine Learning, Health Care, Kidney disease
Implementation plan
Step 1: Initially collect the real-time dataset from UCI. The dataset has a total of 400 samples which had collected from south India.
Step 2: Next, convert all Categorical feature to number by label encoding . As an example is, we label the “normal” and “abnormal” as 0 and 1 respectively.
Step 3: Next, perform pre-processing for balance the missing data’s by using methods like fill and backfill.
Step 4: Next, perform the feature selection and partition the pre-processed dataset into Training data and Testing data.
Step 5: Next, perform the prediction process by using Classifiers Decision Tree and Gaussian Naive Bayes.
Step 6: The performance of this work is measured through the following performance metrics, Confusion Matrix, Accuracy, Sensitivity, Specificity , Precision , Negative Predictive Value , False Positive Rate , False Discovery Rate , False Negative Rate , F1 Score and Matthews Correlation Coefficient
Software Requirement: Python – 3.9.3 and Operating System: Windows 10(64-bit)
- A Machine Learning Methodology for Diagnosing Chronic Kidney Disease
Keywords
Chronic kidney disease, machine learning, KNN imputation, integrated model.
Implementation plan
Step 1: Initially the CKD data set was obtained from the University of California Irvine (UCI).
Step 2: Next, selects several complete samples with the most similar measurements to process the missing data for each incomplete sample by using KNN imputation.
Step 3: Next, perform the feature vector extraction process.
Step 4: Next, based on the feature vector Diagnosing Chronic Kidney Disease by using the machine learning algorithm random forest.
Step 5: Finally, the performance of this work is measured through the following performance metrics, Accuracy, recall, specificity, precision and F1 score
Software Requirement: Python – 3.11.3 and Operating System: Windows 10(64-bit)
- A Novel Approach to Predict Chronic Kidney Disease using Machine Learning Algorithms
Keywords
MachineLearning, Chronic Kidney Disease, Classification, Accuracy, LogisticRegression, Support Vector Machine
Implementation plan
Step 1: Initially collect and load the chronic kidney disease (CKD) dataset that has 25 attributes.
Step 2: Next, perform pre-processing for balance the oversampling datasets
Step 3: Next, the pre-processed dataset divided into two groups into Training data and Testing data.
Step 4: Next, perform the prediction process by using model, which combines the Support Vector Machine, Logistic Regression and K-Nearest Neighbours (KNN) algorithms
Step 5: The performance of this work is measured through the following performance metrics, Accuracy, Precision, Recall, ROC curves and F-Score.
Software Requirement: Python – 3.9.3 and Operating System: Windows 10(64-bit)
- Implementation of Machine Learning Algorithms to Detect the Prognosis Rate of Kidney Disease
Keywords
Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Accuracy
Implementation plan
Step 1: Initially collect the real-time dataset from UCI. The dataset has a total of 400 samples which had collected from south India.
Step 2: Next, The data is pre-processed to make it suitable for the implementation of any machine learning classification algorithms and also handle missing data..
Step 3: Next, perform the feature selection and prediction of chronic kidney disease by using Logistic Regression algorithm, Decision Tree algorithm, Random Forest algorithm, K-Nearest Neighbors algorithm.
Step 4: The performance of this work is measured through the following performance metrics, Accuracy, Precision, Recall,F1-score, Sensitivity, Specificity, false positive rate, False Negative Rate and Negative predictive value.
Software Requirement: Python – 3.11.3 and Operating System: Windows 10(64-bit)
