Disease Detection Using Machine Learning Project

Machine learning techniques to recognize disease are utilized and it is possible to transform healthcare by helping in the previous stage and accurate diagnosis of disease. Writing a research paper in disease detection is not that easy as scholars face pressure on deadline, carry on wide research work and must put in lot of effort. Here at phddirection.com we work as a team and complete all your research work on time. Get a good research proposal on all machine learning topics where it includes introduction, statement of our proposed problem, literature review, objectives, research questions and methods that we carry out to get a conclusion. We have completed more than 3000+ projects on disease detection using machine learning project .We work 24/7 to solve all research queries that you may face.

Here we give a step-by-step guidance on how we take on such specific project:

  1. Define Your Objective:
  • In our work, we aim to identify or categorize by selecting the particular diseases.
  • Initially we are clear about classifying the disease into multiple levels/kinds (multi-class classification) or forecasting the existence of the disease (binary classification).
  1. Data Collection:
  • Public Datasets: Our work utilizes the similar datasets from websites like Kaggle, UCI Machine Learning Repository, or National Institutes of Health.
  • Medical Institutions: To make sure that we have essential approvals and obey all privacy laws, we work together with hospitals or research institutions.
  • Data Type: In our work we take data in the form of images (e.g., X-rays, MRI), genomic patterns, tabular medical records, or even time series (e.g., ECG), these all are based on the disease.
  1. Data Preprocessing:
  • Data Cleaning: Our work cleans the data by handling missing values, outliers and duplicate records.
  • Data Augmentation: To produce more various training samples for image data, we involve the techniques like rotation, zooming, flipping etc.
  • Standardization/Normalization: To standardize/normalize features is necessary especially for tabular data where the structures have various scales.
  • Label Encoding: We alter categorical labels to number patterns.
  • Data Splitting: In our work we split the datasets into three sets namely training, validation and testing.
  1. Feature Engineering:
  • We use the data to remove applicable characteristics such as statistical measurements, field-specific measures (like texture features from images), or others.
  • Our work uses PCA (Principal Component Analysis) if essential, a dimensionality reduction method.
  1. Model Selection and Training:
  • Traditional ML models: In our work we utilize some of the traditional ML techniques like Logistic Regression, Support Vector Machines, Decision Trees, Random Forests and Gradient Boosting Machines etc.
  • Deep Learning: For image data we use CNNs (Convolutional Neural Networks), for sequential data we use RNNs (Recurrent Neural Networks) or even hybrid frameworks are some of the Deep Learning methods.
  • Transfer Learning: Our work fine-tunes the method for a particular task and for image data we utilize pretrained frameworks like (VGG, ResNet).
  • Hyperparameter Tuning: To identify the good parameters for our framework, we aid the use of Grid search or Random search method.
  1. Evaluation:
  • Metrics: Based on the nature of our data and the rate of false positives vs. false negatives, we utilize the metrics like Accuracy, Precision, Recall, F1-Score, ROC curve and AUC.
  • Confusion Matrix: Our work measures the false positives, false negatives, true positives and true negatives.
  • Cross-validation: For a strong evaluation, we utilize k-fold cross validation method.
  1. Deployment:
  • Based on the use-case, we implement our model on various platforms like hospitals, diagnostic centers, or mobile applications.
  • We offer an understandable finding to medical specialists and to make sure that the result is user-friendly.

Project Extensions:

  1. Model Interpretability: To offer awareness into framework decisions, we utilize the methods like SHAP (Shapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanation).
  2. Real-Time Predictions: To work in actual-time for use-cases like continuous health detection, we execute the framework.
  3. Anomaly Detection: To identify anomalies or infrequent diseases, we utilize unsupervised technique rather than supervised learning.


  • Data Privacy & Ethics: To make sure that the security of patient’s data and get all essential approvals and consents, since the medical data is too sensitive.
  • Imbalanced Data: In our work, frequently the number of disease-positive samples is lesser than the negative ones. We utilize methods like SMOTE, ADASYN, or downsampling.
  • Interpretability: Understandable and interpretable findings are the needs for medical specialists.

When working on such projects, we continuously work with field specialists like (doctors or medical researchers). We can get help in diagnosis by utilizing a machine learning framework; the last decision will continuously rest with a qualified medical specialist.

Term paper on disease detection are well written by us as it needs lots of focus on the topic. Preliminary Research on disease detection will be held and our professionals initially carry on the writing. 

Disease Detection Using Machine Learning Topics

Disease detection using machine learning Thesis Ideas

Current thesis topics and ideas on disease detection will be shared to scholars from leading journals as IEEE, writing thesis may be challenging for scholars get our aid by our professional services we complete thesis work in high standards and achieve academic success. We have highly experienced professionals who knows university discipline-specific standards and we will complete your work on time.

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Convolutional Neural Networks, Discrete Wavelet Transform, Principal Component Analysis, Nearest Neighbor, Leaf disease

            In this paper the samples of tomato leaves having disorders. The K-means clustering is introduced for partitioning of dataspace into Voronoi cells. The multiple descriptors viz., Discrete Wavelet Transform, PCA and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally, the extracted features are classified using ML approaches such as SVM, CNN and K-Nearest Neighbor (K-NN).

  1. An artificial intelligence model for heart disease detection using machine learning algorithms


Artificial intelligence, heart disease detection system, Machine learning, Predictive analytics, Random Forest, classifier algorithm

            This paper focuses on AI based heart disease detection using ML algorithm. They used python-based application is developed for healthcare research. They present data processing that entails working with categorical variables, the main phases of application developments are collecting databases, performing logistic regression, and evaluating the dataset’s attributes. A random forest classifier algorithm is developed to identify heart diseases with higher accuracy.

  1. Plant Disease Detection Using Machine Learning Techniques


Support vector machines, Training, Plants (biology), Transfer learning, Crops, Feature extraction

            The difficulty of autonomous illness identification in plants has been solved using traditional ML approaches such as SVM, Multilayer Perception Neural Networks, and Decision Trees. A new plant leaf disease detection technique has been developed based on a transfer learning methodology such as DL, where CNN is employed as a feature extractor and SVM is used for classification.

  1. Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture


Fish Diseases, Aquaculture, Image Processing, Salmon Fish

            In this paper they want to find out the salmon fish disease in aquaculture. This work divides into two portions. In the rudimentary portion, image pre-processing and segmentation have been used to reduce noise and exaggerate the image. In the second portion, they extract the involved features to classify the diseases with the help of the SVM of ML with a kernel function.

  1. Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients


Cardiovascular risk estimation, cardiovascular disease, Conventional risk factors, Ultrasound

            This paper is based on ML based cardiovascular disease (CVD) detection. Two kinds of data were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) and Three kinds of ML classifiers (RF, SVM, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy.

  1. An Effective Heart Disease Detection and Severity Level Classification Model Using Machine Learning and Hyperparameter Optimization Methods


CVD detection, severity classification, hyperparameter optimization, extra trees, imbalance, hyperband.

            This study provides an effective method based on the Synthetic Minority Oversampling Technique (SMOTE) to handle imbalance distribution issue and six different ML classifiers to detect the patient status, and Hyperparameter Optimization (HPO) to find the best hyperparameter for ML classifier together with SMOTE. The results show that SMOTE and Extra Trees (ET) optimized using hyperband gives higher results than other models.

  1. Disease Detection in Cactus (Beles) via the Use of Machine Learning: A Proposed Technique


Image segmentation, Image Color analysis, Transportation, Agriculture

            The early detection of cactus diseases is to prevent the quantitative and qualitative decline of crop yield. Guided filter, and K-means clustering approaches are used to improve the images, noises were removed and images were segmented into better model. Feature extraction approaches were used to extract colour, bag of features, and texture features, respectively. Their proposed ML model will be developed utilising a bag of features and linear SVM.

  1. A machine learning approach for skin disease detection and classification using image segmentation


Skin Disease, Decision Tree, K Nearest Neighbor (KNN)

            They introduce a digital hair removal technique based on morphological filtering such as Black-Hat transformation and inpainting algorithm and then apply Gaussian filtering to de-blur or denoise the images. For extracting input patterns from skin images they used Gray Level Co-occurrence Matrix (GLCM) and statistical features techniques. DT, SVM and KNN were used to extract features and then classify the skin images as melanoma (MEL) etc..

  1. Different stages of disease detection in squash plant based on machine learning


Broad learning, hyperspectral imaging, plant disease detection, powderly mildew, stages of disease

            In this paper, hyperspectral imaging and ML were used to detect different stages (early, middle, and critical stage) of the Powderly mildew disease (PMD) in squash plants. An unmanned aerial vehicle (UAV) was used to collect the data and Locality Preserving Discriminative Broad Learning (LPDBL) was used to distinguish the diseased and healthy plants. The LPDBL gives better result.

  1. Empirical Analysis of Machine Learning Algorithms on Imbalance Electrocardiogram Based Arrhythmia Dataset for Heart Disease Detection.


Heart disease detection, Electrocardiogram (ECG), Empirical analysis, Class imbalance

            This paper exhibits various intelligent solutions for HD detection with an empirical analysis of ML algorithms on ECG based arrhythmia dataset for disease detection. A critical investigation is performed using eight ML algorithms, SVM,K-NN, Random Forest, Extra Tree, Bagging, Decision Tree, Linear Regression, and Adaptive Boosting, under imbalanced and balanced class paradigms and they uses Synthetic Minority Over-sampling Technique for data balancing.

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