liver disease prediction using convolutional neural network and ml model
Deep Learning Projects , Machine Learning Projects , Python Projects

Liver Disease prediction with 99% accuracy using Convolutional Neural Network and ML model

Introduction:

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.

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This project is developed on Python programming with Machine Learning algorithm, K-Nearest Neighbor (KNN) algorithm

The project used Audio pre-processing with MFCC technique to extract feature.

ML is used to train and detect the infant cry type. There are five types used in this project are

Belly Pain

Burping

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The project gets accuracy of 88% with KNN algorithm

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Landslide prediction using Machine Learning algorithms

Landslide is one of the repeated geological hazards during rainy season, which causes fatalities, damage to property and economic losses in all parts of the world. Landslides are responsible for at least 17% of all fatalities from natural hazards worldwide. Due to global climate change, the frequency of landslide occurrence has been increased and subsequently, the losses and damages associated with landslides also have been increased.

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stock price prediction using hyper tuned ml models
Machine Learning Projects , Python Projects

Stock Price Prediction using Hyper tuned ML models

Stock price prediction has long been a challenging problem in the financial industry, with researchers and practitioners continuously seeking more accurate and robust methods. This study explores the application of hyperparameter-tuned machine learning (ML) models for stock price prediction. Leveraging the power of advanced ML algorithms and hyperparameter optimization techniques, this research aims to improve the accuracy of stock price forecasts and provide insights into the effectiveness of different models.

Algorithms Used

Decision Tree algorithm

KNN algorithm

Random forest algorithm

The research begins by collecting historical stock price data, along with relevant financial indicators and market sentiment features. Hyperparameter optimization techniques such as grid search optimization are then applied to fine-tune the models and enhance their predictive performance.

Python Demo

The stock price prediction project also implemented using

Clustering Analysis

www.finalsemprojects.com/stock-prediction-using-clustering-and-classification-model/

Stock price prediction using deep learning project demo can be seen here

www.finalsemprojects.com/stock-price-prediction-in-machine-learning-and-deep-learning/

malaria classification using cnn algorithm deep learning techniques
Deep Learning Projects , Python Projects

Malaria Classification using CNN algorithm-Deep Learning techniques

Malaria Classification using CNN algorithm-Deep Learning techniques with multiple class classification is achieved with good accuracy.

Introduction

This research holds immense significance in the realms of healthcare, epidemiology, and medical research. Malaria, a mosquito-borne infectious disease caused by Plasmodium parasites, poses a significant global health burden, particularly in tropical and subtropical regions. Accurate and timely classification of malaria cases has numerous advantages implications, thus establishing a critical need for precision in this area.

Early Detection and Treatment:

One of the most immediate implications of accurate malaria classification lies in enabling early detection and treatment. By accurately identifying the specific Plasmodium species causing the infection, healthcare providers can tailor treatment strategies accordingly. Different species of Plasmodium respond differently to various antimalarial drugs, which underscores the importance of timely and accurate classification to ensure that patients receive the most effective treatment regimen. This reduces the risk of disease progression and potential complications, thereby enhancing patient outcomes.

Preventing Misdiagnosis:

Malaria’s overlapping symptoms with other febrile illnesses make misdiagnosis a common occurrence, leading to potential adverse consequences, especially in areas with limited access to diagnostic facilities. Accurate classification plays a pivotal role. This ensures that patients receive appropriate care and treatment, effectively mitigating the risk of incorrect therapies. By minimizing the likelihood of misdiagnosis, accurate classification also reduces the unnecessary use of antimalarial drugs, thereby contributing to the broader effort of preventing drug resistance.

Surveillance and Control:

Accurate classification of malaria cases serves as a cornerstone for effective disease surveillance and control strategies. Public health authorities can design and implement targeted interventions that are tailored to the specific malaria dynamics of a region.

Epidemiological Studies:

The significance of accurate malaria classification is magnified in the context of epidemiological studies. By precisely identifying the Plasmodium species involved in infections, researchers gain valuable insights into disease patterns and transmission dynamics. Epidemiological studies that aid in understanding the broader context of malaria transmission, contributing to the formulation of evidence-based public health policies.

Vector Control:

The varied interactions between different Plasmodium species and mosquito vectors underscore the importance of accurate malaria classification. By accurately determining the parasite species responsible for infections, researchers and health authorities can better understand the vector–parasite relationships that play a pivotal role in disease transmission. This understanding, in turn, guides targeted strategies for vector control, thereby interrupting the chain of transmission and reducing the prevalence of the disease.

Drug Resistance Monitoring:

Another crucial aspect where accurate classification comes into play is in monitoring drug resistance. Different Plasmodium species exhibit varying levels of susceptibility to antimalarial drugs. By precisely classifying cases, researchers and healthcare professionals can closely monitor the efficacy of treatments and swiftly detect emerging drug-resistant strains.

Public Health Planning:

Accurate classification of malaria cases plays a pivotal role in informing public health planning efforts. Precise data on disease burden, distribution, and trends are essential for allocating resources for diagnosis, treatment, and prevention programs. By offering a clear picture of the disease landscape, accurate classification ensures that public health initiatives are effectively targeted, thereby maximizing their impact and efficiency.

Global Health Initiatives:

The significance of accurate malaria classification extends to the realm of global health initiatives. With accurate data as a foundation, international efforts to combat malaria, including initiatives by the World Health Organization’s (WHO) Global Malaria Program and collaborative partnerships, gain the necessary insights to formulate effective strategies and allocate resources where they are most needed.

Research Advancements:

Accurate classification of malaria cases not only supports immediate healthcare needs but also propels advancements in medical research. Insights gained from precise parasite identification contribute to ongoing research into the biology, genetics, and epidemiology of Plasmodium parasites. These findings drive the development of new diagnostic tools, treatment strategies, and interventions, thereby pushing the boundaries of medical science.

Improved Patient Outcomes:

In the broader context, the ultimate beneficiaries of accurate malaria classification are the patients themselves. By facilitating timely and tailored treatment, accurate classification reduces morbidity and mortality associated with malaria. Beyond individual patients, accurate classification also contributes to reducing the economic and social burden of malaria on affected communities and societies as a whole.

What are the algorithm Used in the project?

Malaria classification using CNN algorithmis done using two algorithms, one is machine learning and other is deep learning

  1. Decision Tree
  2. Convolution Neural Network (CNN)

What is the project Objective?

The objective of our study is to implement malaria cell type detection system using Convolutional Neural Networks (CNN) from images. There are four classes of detection can be considered, Falciparum, Malariae, Ovale and Vivax detection. Our objective is to implement a machine learning algorithm and a deep learning algorithm and compare their performance. The main aim to classify the given input image to one of four classes by the trained model from input dataset.

What is the number of class or disease types identified?

Falciparum, Malariae, Ovale and Vivax are the types of Malaria disease identified using this project. This is a multi-class classification model. The experimental result shows that our CNN model achieved around 70% of accuracy.

Malaria Classification using CNN algorithm-Deep Learning techniques

How to check the demo of the project?

Please visit under here

Malaria Classification using CNN algorithm-Deep Learning techniques

In conclusion, the significance of accurate malaria classification reverberates across various sectors, from individual patient care to global health initiatives and medical research. Its impact extends to improved treatment, enhanced disease surveillance, and the prevention of misdiagnosis. With its multifaceted implications, accurate malaria classification stands as a pivotal pillar in the global effort to control, prevent, and eventually eradicate malaria.

DO YOU WANT TO CHECK THE PROJECT ABSTRACT? Call 9600095046

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