Disease Prediction Using Machine Learning Project

Designing a machine learning (ML) project is difficult but achieving to detect diseases. The project basically includes various steps ranging from gathering data to applying a framework which makes forecasting. Our writers here will provide you with all the refence papers that we have used for your project. We maintain standard quality throughout the work and timely delivery. The following are the processing steps that we implement to initiate a disease prediction project using ML:

  1. Define Problem

To determine the particular disease we need to detect by using the accessible data and similarity of the disease in people’s health. Our research also examines whether the forecasting the presence or absence of disease by classification and the progression or stage of a disease by regression.

  1. Gather & Prepare the Data

From the dependable sources we collect the data like medical trials, electronic health reports, lab outcomes, patient reviews, biomedical devices, wearable and genomic series. These data consist of the properties such as patient demographics, symptoms, clinical history, genetic details, lifestyle factors and past diagnoses.

  1. Data Pre-processing
  • Data Cleaning: We manage the lost values by eliminating repeated data and rectifying errors and outliers.
  • Transforming Data: By normalization and standardization, we encode both numerical and categorical data.
  • Feature Engineering: When features offer extra understanding, we develop the latest features from traditional ones.
  • Data Augmentation: To enlarge our dataset we employ methods applicable for data types like SMOTE in unstable datasets.
  1. Exploratory Data Analysis (EDA)

For analyzing the features of data we do statistical observations and stimulation like the dispersion of variables, correlation between characteristics and possible detectors of the disease.

  1. Feature Selection

We find the most identical properties which contribute to the forecasting of the disease. Choosing features by utilizing approaches like statistical tests, selection techniques such as backward elimination and ML-based algorithms like feature significance from the tree-based structures.

  1. Choosing Model

Depending on the state of the issue, we select suitable ML frameworks and data. These are the models to detect diseases,

  • Logistic Regression
  • Gradient Boosting Machines (GBM)
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Neural Networks
  • Decision Trees and Random Forest
  1. Model Training & Validation

We instruct the chosen frameworks on the training dataset. Test our models by implementing cross-validation and a hold-out validation set for checking their efficiency. Accuracy, precision, recall, F1-score and ROC-AUC are the metrics we involve in evaluation based on the state of our research questions.

  1. Hyperparameter Tuning

To identify the best working framework we adapt the model parameters and employ methods such as grid search, random search and Bayesian optimization.

  1. Testing

On the validation dataset we test our final model which is not utilized while training and evaluation process to get fairness of its efficiency.

  1. Interpretability

It is essential for us that forecasting of our model is understood. For this we employ methods such as Shapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) to determine the effect of the various characteristics on the detection.

  1. Deployment

We apply our model into a creation platform where it is incorporated by healthcare experts and patients. By this our research includes combining our system into a medical decision assist model and designing a web application.

  1. Monitoring & Maintenance

To make sure our model’s performance and handle its accuracy, we periodically track, retrain and update it when fresh data is accessible.

  1. Ethics & Privacy

Confirm that our project suits all moral regulations and security rules like HIPAA in the US and GDPR in the EU. We hide the patient data and use legal measures to protect the data.

Tools & Libraries

  • For observing data in ML we employ Python and R.
  • Libraries such as scikit-learn, TensorFlow, Keras, and PyTorch are helpful in our ML project.
  • Matplotlib, Seaborn, Plotly support us in visualization.
  • When we require web app deployment, Flask and Django are beneficial.
  • For manipulating data Pandas are useful to us.
  • To apply and store our model when needed we use cloud services
  • In containerization of the app, Docker assists us in our model.

Final Notes:

It is essential to recognize that our detections are possible and utilized to support, not replace, expert’s clinical advice and diagnosis, when ML is robust in forecasting diseases. Additionally ML models maintain the unfairness when the training data is not an admin, so it is crucial to focus on high-quality and different data.

Disease Prediction Using Machine Learning Topics

Disease Prediction Using Machine Learning Project Thesis Ideas

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  1. Machine Learning Approach for Estimation and Novel Design of Stroke Disease Predictions using Numerical and Categorical Features


Stroke prediction, Stroke disease analysis, Machine learning

Our paper utilizes ML methods where children to adult age data taken in which we can extract the data and after we collected all the data, we can run it on various algorithms. We utilized the machine learning methods like RF, DT, SVM and LR methods to train various methods and compare the outcome for the fine prediction method. Among all our RF, LR and SVM method gives best accuracy.

  1. Automated Disease Prediction Using Machine Learning Technology


Health Care, Supervised Machine Learning, Diseases Prediction, KNN

We have established supervised machine learning methods potential in surpassing current disease diagnosis approach and supporting healthcare labours in early findings of high risk condition. Our suggested system estimates the user-offered symptoms as inputs and outputs the likelihood of condition. We execute various machine learning to calculate the performance on predicting diseases. We also utilized DT, NB etc. to  estimate the Best ML method to predict the disease.

  1. Heart Disease Prediction Using Machine Learning


Cardiovascular disease (CVD), Artificial intelligence, Support Vector Machine (SVM)

Our paper concentrates on patients who has suffer more from this based on their different medical features. We have to suggest a heart disease prediction system which can be utilized to diagnose the patients whether they are victim or not by utilizing the earlier medical feature of patient. SVM and KNN methods in ML can be utilized to predict and categorize the patients with heart disease. Our KNN and SVM model gives high accuracy than previous study namely NB method etc.

  1. Prediction of Cardiovascular Disease Risk using Machine Learning Models


Cardiovascular, Early Diagnosis, Data Processing, Random Forest, Logistic Regression

We offer a ML method to predict the heart disease of a person by the analysis of large dataset. We utilise the data set to predict the heart disease in future considering earlier data. We used RF, LR, DT and SVM methods on large datasets. We can predict with Random Forest has displays the higher accuracy through prediction.

  1. Inflammation of Liver and Hepatitis Disease Prediction using Machine Learning Techniques


ANN (Artificial Neural Networks), UML (Unified Modelling Language)

Our study aims to design a system for detection and diagnosis of hepatitis disease. It is essential to combine AI with healthcare and it aids in the detection of disease in previous phase already it deteriorates the body. We present a detailed study between ML methods like SVM, KNN and ANN were considered to classify the data points into relevant classes and to predict and diagnosis of hepatitis. Our system is well trained with efficient data and it will useful to our real life.

  1. Prediction of Myositis Disease using Machine Learning Algorithm


Myositis, prediction, Decision tree, Naive bayes, Gradient boosting

The goal of our paper is to estimate the performance of different machine learning methods namely Gradient Boosting, DT, RF, LR, NB and SVM in predicting myositis disease. We utilize the data set in our work that consists of clinical and demographic details of patients. Our Gradient Boosting method gives the best performance on accuracy.

  1. Plant Leaf Diseases Prediction using Butterfly Optimization BO with Support Vector Machine SVM


Leaf diseases, Butterfly optimization BO, segmentation, pre-processing

Our paper discovers some of the difficulties that can be arise when using machine learning method to find the plant disease and pests in real-world settings. We can obtain the features that can be classified by utilizing the machine learning methods like Butterfly Optimisation BO with Support vector machine SVM. We advised the user to receive treatment at final stage and the farmers have the best chance to maintain their crops.

  1. MedAi: A Smartwatch-Based Application Framework for the Prediction of Common Diseases Using Machine Learning


Healthcare, smartwatch, mobile application

Our paper offer a smartwatch-based prediction model named ‘MedAi’ for multiple diseases namely heart disease, hypertension, stroke etc by utilizing machine learning methods. It comprises of three core models a prototype smartwatch, a ML method to analyse the data and make prediction and an mobile application to predict the result. We used several ML methods namely SVM, SVR, KNN, XGBoost, LSTM and RF to examine the best method. Our RF method outperforms other ML methods.

  1. Disease Prediction Using Symptoms based on Machine Learning Algorithms and Natural Language Processing


GUI, Chatbot

We can predict the disease of a patient by utilizing several ML methods like NB, RF and DT. We implement a Chatbot as the communication will be difficult. We can do this by utilizing Natural Language Processing (NLP). Our ending result will displays interface utilizing three ML methods and feature extraction can depend upon the symptom. We proposed the five approaches, at first we have to preprocess the dataset, next DT is utilized to generate a prediction. Next RF for forecast the illness, NB is used in fourth model and at last NLP for Chatbot, the output from all models taken to identify the best method.

  1. RespoBot: Chatbot used for the prediction of diseases using Machine Learning and Deep Learning with respect to Covid-19


Voting Classifier (VC)

We can predict the disease by utilizing the ML methods like LR, SVM, RF, Stochastic Gradient Descent, Gradient Boosting, DT, Naïve Bayes Classifier and Voting classifier ensemble approaches. After prediction we have to compare the analysis in terms of accuracy. We can use a Neural Network based Chatbot that utilizes Natural Language Processing.