As a final year student, choosing a compelling and impactful project is crucial for showcasing your skills and knowledge. Liver disease prediction with 99% accuracy is good to choose, an area of growing significance in the healthcare field, offers an excellent opportunity to apply advanced machine learning techniques. In this blog post, we will explore how Convolutional Neural Networks (CNNs) and Decision Trees (DT) can be harnessed to predict liver disease, presenting an exciting and relevant final year project idea.

  1. Understanding Liver Disease:
    Liver disease, is the more life threatening disease. It is important to detect early. The early detection helps in extending life of patients.
  2. Convolutional Neural Networks (CNNs):
    2.1. Introduction to CNNs: CNNs as a powerful deep learning technique widely used in computer vision applications. CNN has convolutional layers, pooling layers, and fully connected layers.
    2.2. CNNs for Liver Disease Prediction: Explore how CNNs can be adapted for liver disease prediction by utilizing medical images such as ultrasound or MRI scans. Discuss the steps involved in data preprocessing, model architecture design, training, and evaluation.
    2.3. Benefits and Limitations of CNNs: The advantages of CNNs in capturing intricate patterns in medical images, enabling high accuracy in disease prediction. Potential limitations are the need for sufficient data and computational resources.
  3. Decision Trees (DT):
    3.1. Introduction to Decision Trees: Decision trees is a versatile machine learning algorithm used for classification tasks.
    3.2. DT for Liver Disease Prediction: The application uses decision trees in predicting liver disease .
    3.3. Benefits and Limitations of DT: Decision trees has ease of interpretability, ease of implementation, and handling missing values. It has the potential limitations, such as overfitting and sensitivity to small changes in the training data.
  4. Comparative Analysis:
    Compare the performance and characteristics of CNNs and decision trees for liver disease prediction. Liver disease prediction with 99% accuracy whereas Decision tree achieved very less than this. Discuss their strengths and weaknesses in terms of accuracy, interpretability, computational requirements, and suitability for different data types. Encourage students to consider the trade-offs when choosing between these two approaches.
  5. Conclusion:
    Liver disease prediction with 99% accuracy using Convolutional Neural Networks in predicting liver disease is more accurate than using machine learning technique Decision Tree.

Python demo video

Check some of the related project demo

Liver Disease Prediction using Deep learning Models

3 thoughts on “Liver Disease prediction with 99% accuracy using Convolutional Neural Network and ML model

  1. If some one needs expert support on the project topic this project center guiding us better. I have gained good knowledge on my final year project in be cse graduation. Thanks to the team

  2. Hi, i feel that i saw your videos are really looking good and learnable. what is the accuracy of this project? do we have web application?

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