Diagnosis of Chronic Kidney Disease Using Machine Learning Algorithm

A large percentage of people globally suffer from chronic kidney disease (CKD), a serious health concern. Effective diagnosis, treatment, and referral of CKD depend heavily on early identification and prediction of the disease. However, it is difficult to evaluate and derive significant insights from health data due to its vast and complicated nature. Engineers and medical researchers are using data mining techniques and machine learning algorithms to create predictive models for chronic kidney disease (CKD) in an effort to address this issue. The goal of this research is to create and validate predictive models for chronic kidney disease (CKD) based on a variety of clinical factors, including albuminuria, age, diet, eGFR, and pre-existing medical problems. The objective is to estimate the likelihood of renal failure, which may necessitate kidney dialysis or a transplant, and to evaluate the degree of kidney disease. With the use of this knowledge, patients and healthcare providers should be able to make well-informed decisions about diagnosis, treatment, and lifestyle changes. Patterns in the gathered data can be found, and future incidence of CKD or other related diseases can be predicted, by utilising MLT such as ANN and data mining techniques. Finding novel characteristics linked to the onset of renal disease and adding more trustworthy data from CKD patients. The best algorithm to categorise the data as CKD or NOT_CKD is chosen throughout the design process, and the data is then classified according to this differentiation. Estimated glomerular filtration rate (eGFR), which offers important details about the patient's current kidney function, is used to classify cases of chronic kidney disease. By combining complete patient data with machine learning algorithms, this research advances the diagnosis of chronic kidney disease (CKD) and improves patient outcomes

Automatic segmentation of skin lesions from dermatological photographs

Melanoma is the deadliest form of skin cancer if left untreated. Incidence rates of melanoma have been increasing, especially among young adults, but survival rates are high if detected early. Unfortunately, the time and costs required for dermatologists to screen all patients for melanoma are prohibitively expensive. There is a need for an automated system to assess a patient's risk of melanoma using photographs of their skin lesions. Dermatologists could use the system to aid their diagnosis without the need for special or expensive equipment. One challenge in implementing such a system is locating the skin lesion in the digital image. Most existing skin lesion segmentation algorithms are designed for images taken using a special instrument called the dermatoscope. The presence of illumination variation in digital images such as shadows complicates the task of finding the lesion. The goal of this research is to develop a framework to automatically correct and segment the skin lesion from an input photograph. The first part of the research is to model illumination variation using a proposed multi-stage illumination modeling algorithm and then using that model to correct the original photograph. Second, a set of representative texture distributions are learned from the corrected photograph and a texture distinctiveness metric is calculated for each distribution. Finally, a texture-based segmentation algorithm classifies regions in the photograph as normal skin or lesion based on the occurrence of representative texture distributions. The resulting segmentation can be used as an input to separate feature extraction and melanoma classification algorithms. The proposed segmentation framework is tested by comparing lesion segmentation results and melanoma classification results to results using other state-of-the-art algorithms. The proposed framework has better segmentation accuracy compared to all other tested algorithms. The segmentation results produced by the tested algorithms are used to train an existing classification algorithm to identify lesions as melanoma or non-melanoma. Using the proposed framework produces the highest classification accuracy and is tied for the highest sensitivity and specificity

High-Level Intuitive Features (HLIFs) for Melanoma Detection

Feature extraction of segmented skin lesions is a pivotal step for implementing accurate decision support systems. Existing feature sets combine many ad-hoc calculations and are unable to easily provide intuitive diagnostic reasoning. This thesis presents the design and evaluation of a set of features for objectively detecting melanoma in an intuitive and accurate manner. We call these "high-level intuitive features" (HLIFs). The current clinical standard for detecting melanoma, the deadliest form of skin cancer, is visual inspection of the skin's surface. A widely adopted rule for detecting melanoma is the "ABCD" rule, whereby the doctor identifies the presence of Asymmetry, Border irregularity, Colour patterns, and Diameter. The adoption of specialized medical devices for this cause is extremely slow due to the added temporal and financial burden. Therefore, recent research efforts have focused on detection support systems that analyse images acquired with standard consumer-grade camera images of skin lesions. The central benefit of these systems is the provision of technology with low barriers to adoption. Recently proposed skin lesion feature sets have been large sets of low-level features attempting to model the widely adopted ABCD criteria of melanoma. These result in high-dimensional feature spaces, which are computationally expensive and sparse due to the lack of available clinical data. It is difficult to convey diagnostic rationale using these feature sets due to their inherent ad-hoc mathematical nature. This thesis presents and applies a generic framework for designing HLIFs for decision support systems relying on intuitive observations. By definition, a HLIF is designed explicitly to model a human-observable characteristic such that the feature score can be intuited by the user. Thus, along with the classification label, visual rationale can be provided to further support the prediction. This thesis applies the HLIF framework to design 10 HLIFs for skin cancer detection, following the ABCD rule. That is, HLIFs modeling asymmetry, border irregularity, and colour patterns are presented. This thesis evaluates the effectiveness of HLIFs in a standard classification setting. Using publicly-available images obtained in unconstrained environments, the set of HLIFs is compared with and against a recently published low-level feature set. Since the focus is on evaluating the features, illumination correction and manually-defined segmentations are used, along with a linear classification scheme. The promising results indicate that HLIFs capture more relevant information than low-level features, and that concatenating the HLIFs to the low-level feature set results in improved accuracy metrics. Visual intuitive information is provided to indicate the ability of providing intuitive diagnostic reasoning to the user

Dermal Radiomics: a new approach for computer-aided melanoma screening system

Skin cancer is the most common form of cancer in North America, and melanoma is the most dangerous type of skin cancer. Melanoma originates from melanocytes in the epidermis and has a high tendency to develop away from the skin surface and cause metastasis through the bloodstream. Early diagnosis is known to help improve survival rates. Under the current diagnosis, the initial examination of the potential melanoma patient is done via naked eye screening or standard photographic images of the lesion. From this, the accuracy of diagnosis varies depending on the expertise of the clinician. Radiomics is a recent cancer diagnostic tool that centers around the high throughput extraction of quantitative and mineable imaging features from medical images to identify tumor phenotypes. Radiomics focuses on optimizing a large number of features through computational approaches to develop a decision support system for improving individualized treatment selection and monitoring. While radiomics has shown great promise for screening and analyzing di erent forms of cancer such as lung cancer and prostate cancer, to the best of our knowledge, radiomics has not been previously adopted for skin cancer, especially melanoma. This work presents a dermal radiomics framework, which is a novel computer-aided melanoma diagnosis. While most computer-aided melanoma screening systems follow the conventional diagnostic scheme, the proposed work utilizes the physiological biomarker information. To extract physiological biomarkers, non-linear random forest inverse light-skin interaction model is proposed. The construction of dermal radiomics sequence is followed using the extracted physiological biomarkers, and the dermal radiomics framework for melanoma is completed by constructing diagnostic decision system based on random forest classi cation algorithm