Disease Prediction Using Machine Learning Project

In the development of disease forecasting model based on machine learning (ML), various processing flows are included by us. Our research experts offer a steadfast support for scholars throughout the decision-making process we stand as your shadow and provide writing support whenever you need it so get your machine learning projects done by us. To elaborate this context, here we frame about a hypothetical environment in that the diabetes disease is forecasted by considering several health factors.

  1. Problem Discussion:

In terms of various factors like BMI, age, glucose range and blood pressure, we forecasted whether the diabetes level of patient will increase or not in one year.

  1. Collection of Data:

Pima Indians dataset are utilized by us that has the patient’s details with several features such as:

  • Glucose level
  • Insulin level
  • Number of pregnancies
  • Age
  • Outcome ( if the diabetes level increased)
  • Diabetes pedigree function
  • Skin thickness
  • BMI
  • Blood pressure
  1. Preprocessing of Data:
  • Managing Missing Values: For managing missing data, we utilized imputation, deletion and other approaches.
  • Feature Scaling: If the utilized methods such as KNN or SVM sensitive to feature scales, the features are normalized or standardized by us.
  • Splitting of Data: In our study, we split the dataset into three sets like training, validation and testing.
  1. Exploratory Data Analysis (EDA):
  • To interpret the distribution of various features, we utilized visualizations.
  • Among features and the target attributes, the potential correlations are detected by us.
  1. Selection of Features:

We detect the very important features related to forecasting process by employing different techniques including:

  • Recursive Feature Elimination (RFE)
  • Correlation Matrix
  • Feature importance from tree-based methods.
  1. Model Selection:

Various ML based methods are evaluated by us to find out the optimal method by considering validation data:

  • Decision Trees & Random Forest
  • Neural Networks
  • K-Nearest Neighbors (KNN)
  • Logistic Regression
  • Support Vector Machines (SVM)
  • Gradient Boosted Machines such as LightGBM and XGBoost.
  1. Model Training:

By using training dataset, we train the selected model.

  1. Evaluation:

We considered important metrics to examine the efficiency of our framework by utilizing test dataset. Several metrics such as precision, accuracy, ROC-AUC, and F1-score are evaluated and to reduce the false negatives, recall metrics is considered.

  1. Optimization:
  • Hyperparameter Tuning: For this, we employed methods such as Random search or grid search.
  • Feature Engineering: In terms of understanding of related field skills and EDA, the features are developed or converted by us.
  • Integrated Techniques: In this, various frameworks’ forecasting are integrated.
  1. Deployment:

After the effective performance of our framework, we can implement our framework in the suitable environments like medical’s software system or hospital.

  1. Monitoring and Feedback Loop:

The actual time efficiency of our framework is tracked in our research and by using new data, we reconstruct the framework. To examine and enhance the forecasting process, we make a review loop with clinical experts.

  1. Ethical and Practical Considerations:
  • Confidentiality and Data Safety: We must check whether the clinical related data are stored safely and obey some rules such as HIPAA.
  • Understanding: The frameworks or tools like LIME or SHAP values that provide high understanding about the medical based critical decisions are utilized by us.
  • Collaboration: Associate with medical experts or staffs are the most important one. Through this, we can make sure about the forecasting of our framework.
  • False Negatives or False Positives: We should interpret the importance of false positive and negative cases. Here, the false negative cases are more serious than the false positive cases.

Libraries and Tools:

  • Data Handling and EDA: For this, we used pandas, Matplotlib, NumPy and Seaborn.
  • Machine Learning: Various approaches such as TensorFlow, Scikit-learn, Keras and XGBoost are employed by us.
  • Interpretability: For better understanding, we utilized LIME and SHAP.

Final Conclusion:

We employ Machine Learning as an additional technique with previous clinical evaluation and skills in addition to help in disease forecasting. We make sure about the accuracy and utilization of our framework by offering consistent validation, reconstruction and association with medical experts. We exactly maintain an orderly approach to confirm logical flow and evenness throughout your academic paper. We guarantee that your concepts, ideas, and citations will be stated correctly. Custom Research topics will be handled well by our research experts.

Disease Prediction Using Machine Learning Project Topics

Disease Prediction Using Machine Learning Research Topics 

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

Keywords:

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

Abstract:

Majority of medical misfortunes are a result of inaccurate diagnosis. Medical professionals may find difficulty to effectively diagnose diseases and assess symptoms at an early stage due to the lack of time with the patients and the lack of resources to recognize huge amounts of data on time. Machine learning (ML) can help in analyzing large datasets and identify patterns that are usually not visible to human eyes. It reduces cost and repetitive tasks to provide a more personalized diagnosis. In this paper., supervised machine learning (ML) algorithms have demonstrated potential in surpassing current disease diagnosis techniques and supporting healthcare workers in the early identification of high-risk conditions. The proposed system evaluates the user-provided symptoms as input and outputs the likelihood of the condition. It implements different machine learning models to evaluate their performance on predicting diseases. In addition., algorithms like the decision tree algorithm., Naive Bayes algorithm etc. is also done to determine the best ML algorithm forthe prediction of diseases.

Keywords:

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

Keywords:

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

Keywords:

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 utilize 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

Keywords:

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

Keywords:           

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

Keywords:

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

Keywords:

Healthcare, smartwatch, mobile application

Our paper offers 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 a 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

Keywords:

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

Keywords:

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.