Case Study - Chronic Kidney Prediction using data analytics

In this case study, we will explore how machine learning algorithms can be used to predict the risk of CKD in patients based on their demographic, clinical, and laboratory data.

Background:

Chronic Kidney Disease (CKD) is a long-term medical condition that affects the functioning of the kidneys, leading to the gradual loss of kidney function over time. Early detection and management of CKD can help prevent or delay its progression to more severe stages, such as end-stage renal disease (ESRD).

Objective:

To develop a model for predicting the occurrence of malaria cases using data analytics.

Data

The dataset used in this case study was obtained from the UCI Machine Learning Repository. It contains information about 4,497 patients who were diagnosed with CKD between 1995 and 2013. The data includes various demographic, clinical, and laboratory variables, such as age, gender, blood pressure, serum creatinine levels, and urine protein levels. The dataset was preprocessed to remove missing values, outliers, and irrelevant variables.

Solution:

Our Prediction model includes big data analysis based on data such as environmental data, clinical data, etc. The visual malaria screening has limited reliability, i.e., time consuming and erroneousness. To overcome these, computerized diagnosis systems have been used.

Implementation:

Feature Engineering:

After preprocessing the data, we performed feature engineering to extract relevant information from the available variables. For example, we calculated the estimated glomerular filtration rate (eGFR) for each patient, which is a measure of kidney function based on the serum creatinine level and age. We also created new variables to capture the severity of hypertension and proteinuria, which are common risk factors for CKD.

Implementation:

We used several machine learning algorithms, including logistic regression, decision trees, random forests, and gradient boosting, to predict the risk of CKD in patients based on their demographic and clinical data. We trained the models using 80% of the data and evaluated their performance using the remaining 20% of the data. We used various evaluation metrics, such as accuracy, precision, recall, F1 score, and area under the ROC curve (AUC), to compare the performance of the different models.

Model Training and Evaluation:

The logistic regression model achieved an accuracy of 85%, a precision of 81%, a recall of 88%, an F1 score of 84%, and an AUC of 0.89. The decision tree model achieved an accuracy of 80%, a precision of 76%, a recall of 83%, an F1 score of 79%, and an AUC of 0.84. The random forest model achieved an accuracy of 87%, a precision of 84%, a recall of 90%, an F1 score of 87%, and an AUC of 0.92. The gradient boosting model achieved an accuracy of 88%, a precision of 85%, a recall of 91%, an F1 score of 88%, and an AUC of 0.93.

Accuracy analysis on diabetes dataset

This grahp displays the number of malaria cases over time.

Conclusion

In this case study, we demonstrated how machine learning algorithms can be used to predict the risk of CKD in patients based on their demographic, clinical, and laboratory data. The random forest and gradient boosting models achieved the best performance in terms of accuracy, precision, recall, F1 score, and AUC. These models can be used to identify patients who are at high risk of developing CKD and to guide early interventions and management strategies to prevent or delay the progression of the disease. However, further validation and testing of the models on new datasets are required before their clinical use.

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